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JSON files for test_class_optimize.py Update test_class_optimize.py added Skipped per default Fix optimization test Fix optimization test
2024-10-06 18:16:43 +02:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-08 15:42:18 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-08 15:42:18 +01:00
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2024-12-12 14:37:46 +01:00
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chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-08 15:42:18 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
2024-12-15 15:32:58 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
2024-12-15 15:32:58 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
2024-12-15 15:32:58 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
1,
Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
1,
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
2024-12-15 15:32:58 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
2024-12-15 15:32:58 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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2026-03-17 12:41:15 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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feat: add Home Assistant and NodeRED adapters (#764) Adapters for Home Assistant and NodeRED integration are added. Akkudoktor-EOS can now be run as Home Assistant add-on and standalone. As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard in Home Assistant. The fix includes several bug fixes that are not directly related to the adapter implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: development version scheme The development versioning scheme is adaptet to fit to docker and home assistant expectations. The new scheme is x.y.z and x.y.z.dev<hash>. Hash is only digits as expected by home assistant. Development version is appended by .dev as expected by docker. * fix: use mean value in interval on resampling for array When downsampling data use the mean value of all values within the new sampling interval. * fix: default battery ev soc and appliance wh Make the genetic simulation return default values for the battery SoC, electric vehicle SoC and appliance load if these assets are not used. * fix: import json string Strip outer quotes from JSON strings on import to be compliant to json.loads() expectation. * fix: default interval definition for import data Default interval must be defined in lowercase human definition to be accepted by pendulum. * fix: clearoutside schema change * feat: add adapters for integrations Adapters for Home Assistant and NodeRED integration are added. Akkudoktor-EOS can now be run as Home Assistant add-on and standalone. As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard in Home Assistant. * feat: allow eos to be started with root permissions and drop priviledges Home assistant starts all add-ons with root permissions. Eos now drops root permissions if an applicable user is defined by paramter --run_as_user. The docker image defines the user eos to be used. * feat: make eos supervise and monitor EOSdash Eos now not only starts EOSdash but also monitors EOSdash during runtime and restarts EOSdash on fault. EOSdash logging is captured by EOS and forwarded to the EOS log to provide better visibility. * feat: add duration to string conversion Make to_duration to also return the duration as string on request. * chore: Use info logging to report missing optimization parameters In parameter preparation for automatic optimization an error was logged for missing paramters. Log is now down using the info level. * chore: make EOSdash use the EOS data directory for file import/ export EOSdash use the EOS data directory for file import/ export by default. This allows to use the configuration import/ export function also within docker images. * chore: improve EOSdash config tab display Improve display of JSON code and add more forms for config value update. * chore: make docker image file system layout similar to home assistant Only use /data directory for persistent data. This is handled as a docker volume. The /data volume is mapped to ~/.local/share/net.akkudoktor.eos if using docker compose. * chore: add home assistant add-on development environment Add VSCode devcontainer and task definition for home assistant add-on development. * chore: improve documentation
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
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Update optimize full-run (#238) * Enable full-run in github workflow
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
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Update optimize full-run (#238) * Enable full-run in github workflow
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
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2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
582.6180000000041,
fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
0.0,
fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
59.810111380126784,
Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
0.0,
Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
99.72409090909093,
Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
0.0,
fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
2024-12-15 15:32:58 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
69.12409090909085,
Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
47.952272727272714,
Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
16.01263877609336,
fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
55.946263193163475,
Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
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optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. (#242) * optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull. * test_class_optimize: Update testdata * Write pdf and json to test/testdata/new.... so it can be analyzed manually or just copied as new expected result. * workflow: Upload pytest optimization result artifacts (pdf, json)
2024-12-15 15:32:58 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
]
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Nested config, devices registry * All config now nested. - Use default config from model field default values. If providers should be enabled by default, non-empty default config file could be provided again. - Environment variable support with EOS_ prefix and __ between levels, e.g. EOS_SERVER__EOS_SERVER_PORT=8503 where all values are case insensitive. For more information see: https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values - Use devices as registry for configured devices. DeviceBase as base class with for now just initializion support (in the future expand to operations during optimization). - Strip down ConfigEOS to the only configuration instance. Reload from file or reset to defaults is possible. * Fix multi-initialization of derived SingletonMixin classes.
2025-01-12 05:19:37 +01:00
"device_id": "ev1",
chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-08 15:42:18 +01:00
"hours": 48,
Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
"charge_array": [
chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-08 15:42:18 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
],
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chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-08 15:42:18 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
],
translation of battery.py v3 (#262)
2024-12-19 14:50:19 +01:00
"discharging_efficiency": 1.0,
"capacity_wh": 60000,
"charging_efficiency": 0.95,
"max_charge_power_w": 11040,
Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
"soc_wh": 32400.000000000004,
translation of battery.py v3 (#262)
2024-12-19 14:50:19 +01:00
"initial_soc_percentage": 54
Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
},
"start_solution": [
Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
1.0,
fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
2.0,
fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Self consumption predictor * Inverter: Self consumption interpolator for better discharge_hour results * Small penalty when EV 100% and charge >0 * Price Forceast (use mean of last 7 days instead of repeat) * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
2024-12-19 14:45:20 +01:00
2.0,
fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949) * record battery SOC at the start of the interval for accurate display * map battery charge rates to GeneticOptimizationParameters and update related logic Instead of using the EV charge rates, the battery now has its own charge rate defined in the GeneticOptimizationParameters to better separate those entities. * separate raw gene values from SOC-clamped op factors The genetic_*_factor columns in the OptimizationSolution dataframe now always carry the raw gene values (optimizer intent), while the battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction operation_mode_factor reflect SOC-clamped effective values that can actually be executed given the battery's state of charge at each hour. Adds GeneticSolution._soc_clamped_operation_factors(): - AC charge factor: proportionally scaled down when battery headroom (max_soc - current_soc) is less than what the commanded factor would store in one hour; zeroed when battery is full. - DC charge factor: zeroed when battery is at or above max SOC. - Discharge: blocked when SOC is at or below min SOC. Both optimization_solution() and energy_management_plan() pass the clamped values to _battery_operation_from_solution(), so the plan instructions the HEMS receives reflect physically achievable targets. * ensure max AC charge power is only used if defined * update fixtures for PV suffix + SOC-clamp algorithm changes * handle None case for home appliance start hour in simulation if the hour is zero, the the home appliance wont start * update handling of invalid charge indices in autocharge hours Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one) * remove double code in simulation preparation and improve comments * update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>
2026-03-17 12:41:15 +01:00
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Update optimize full-run (#238) * Enable full-run in github workflow
2024-12-12 14:37:46 +01:00
],
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chore: eosdash improve plan display (#739) * chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-08 15:42:18 +01:00
}
Reference in New Issue Copy Permalink