A planned battery export kept moving 15 minutes later with every run. At
07:49 the plan exported at 08:00 and 08:15; the run at 08:00 exported at 08:15
and 08:30, although nothing in the forecasts had changed.
Genomes are run-relative: gene 0 controls the slot the run starts in. Clients
such as Node-RED send the previous `start_solution` back unchanged, so after a
slot boundary every decision in it is read one slot too late. The warm start is
seeded as exact copies and local neighbours, and when an export 15 minutes
later scores almost the same, the shifted genome survives and becomes the next
warm start. The internal energy management run reused its last solution the
same way.
Solutions now carry `start_solution_datetime`, the start of the slot gene 0
controls, and requests accept it back. Before seeding, the battery and EV blocks
drop the elapsed slots and repeat their last gene. A warm start that starts
after the run, or whose control slots have all elapsed, is ignored. When a
request omits the datetime but its `start_solution` equals the last solution of
this server, that solution's start is used, so existing clients are fixed
without changes. Appliance genes index per-run start slot lists that cannot be
rebuilt for the earlier run and stay as they are, validated as before.
On a converged population the boost was permanently on, so there was nothing
left for it to intervene in. Its trigger, DIVERSITY_BOOST_THRESHOLD at 0.35, sat
above SELECTION_DIVERSITY_FLOOR at 0.30 - the floor the selection itself
guarantees - so `diversity < threshold` was true in every generation after
convergence. The threshold now sits below the floor.
The immigrants the boost injects are by construction the worst individuals in
the pool, and `_select_diverse` ran a plain tournament over parents and
offspring together, so they were removed in the very generation that created
them and their genes never recombined. A bounded share of seats
(IMMIGRANT_PROTECTION_FRACTION) is now reserved for them for
IMMIGRANT_PROTECTION_GENERATIONS selections. The incumbent is protected by
genome key, so no immigrant can evict the best solution or an equal-genome twin,
and offspring do not inherit the protection.
The log line was edge-triggered on `diversity_boost_active`, which was also
cleared on every fitness improvement, so a running boost re-announced itself
with "stagnation 0" while a boost that never stopped looked like several short
ones. It now tracks the boost alone, and the end of a boost is logged too.
Measured on a converged population: without protection 0 of 12 immigrants
survive the selection, with it all 12 do, and the incumbent is kept either way.
The optimizer treated the end of `optimization.horizon_hours` as the end of the
world: energy left in the battery there was worth a single configured price per
kWh, so it either dumped the battery into the last hours or hoarded it,
depending on that one number.
The horizon is now two spans. `horizon_hours` still receives every control
command. The new `optimization.tail_horizon_hours` (default 48 h) is a pure
lookahead that never produces a command. In AUTO terminal-value mode a
deterministic dynamic program solves that tail backwards on a 101-point SoC
grid using the production battery and inverter models - SoC bounds, power caps,
conversion losses, configured charge and export rates, direct-marketing
permission and LCOS on delivered DC energy - and the existing AUTO proxy
supplies the continuation value at the tail end. Genetic fitness reads the
resulting curve. `tail_horizon_hours: 0` restores the plain proxy at the control
end, FIXED is unchanged.
The forecast budget is reported, never enforced by refusal: a tail that does not
fit is shortened to what the forecast covers and reported as
`effective_tail_hours`, and a control horizon that does not fit is warned about
at configuration time and rejected by the optimizer at run time, which knows
which series ran out. `prediction.hours` defaults to 72 so the new defaults fit
out of the box; existing shorter configurations keep starting.
Control arrays and warm-start genomes now begin at the run timestamp rather than
midnight, flagged by `controls_start_at_now` so the adapters still read older
solutions. `forecast_interval_seconds` declares the resolution of shortened
native quarter-hour inputs.
Required forecasts are no longer silently replaced by demo providers. A missing
PV, price, load, feed-in or weather forecast used to rewrite the configured
provider and retry, so a run could quietly optimize against invented data.
Missing values now stay missing, and provider values are held only within their
own source interval instead of being extended indefinitely.
Also fixes a config update that could leave EOS half-updated: the merged
candidate is validated before the singleton is reinitialized.
Four provider tests that hard-coded the old 48 h prediction default are rewritten
to derive their expectations from the configured horizon.
A run whose price forecast is all zeros produces no priced residual load,
so AUTO cannot derive a curve and quietly credits the request scalar
instead. The reported mode was then "FIXED" - indistinguishable from a
run actually configured that way.
The solution now carries a reason, and the fallback is logged as a
warning instead of passing unnoticed.
The energy still stored when the horizon ends keeps its worth: it replaces
grid imports that are paid for afterwards. Crediting that with a single
price per kWh cannot describe it, because the value is not linear in the
amount stored. The first kWh replaces the most expensive hour that PV
cannot cover, the next one the second most expensive, and once every such
hour is served, further energy replaces nothing.
A scalar has to pick one slope for all of it. High enough for the first kWh
means hoarding a full battery; low enough for the last kWh means running it
empty by the end of the horizon - which is exactly what the previous default
of 0 EUR/kWh did.
terminal_value_mode = AUTO (the new default) builds the curve instead. There
is no forecast beyond the horizon, so its trailing window stands in for the
day that follows: residual load max(load - PV, 0) per slot, priced at its
import price, sorted and accumulated. LCOS is subtracted from every marginal
value so stored energy is not credited twice, and the tail beyond the
residual load is only credited when direct marketing allows an export. The
curve is built once per run; the search only interpolates on it.
The solution reports what a run used as terminal_value, curve included, so
the shape can be inspected instead of guessed. FIXED restores the previous
scalar behaviour.
In a 48 h scenario with two cheap slots at the end, AUTO keeps the battery
at 50 % and credits 3.85 EUR where FIXED with 0 EUR/kWh drains it to empty.
The stored optimization results move accordingly - the objective changed.
With grid charging switched off (inverter.max_ac_charge_power_w = 0) the
simulation zeroes the AC charge array before using it, but it did so by
rebinding a local name. The solution is read back from the original array,
so it still carried the optimizer's AC charge genes - genes the fitness
never evaluated, because the simulation ignored them, and which are
therefore arbitrary.
The effect was visible as a slot with ac_charge = 0.8 where the battery SoC
does not move. Harmless inside EOS, but a controller that follows the plan
would grid-charge the battery at a time nobody planned or paid for.
Zero the array in place so the reported plan matches what was simulated.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Three related scheduling improvements, all opt-in and behaviour-preserving
when the new fields are not set.
Flexible consumers get absolute time bounds next to the recurring
time_windows: earliest_start_datetime and deadline_datetime, where the
deadline requires the complete run to have *finished* before that moment
("clean dishes by 03:00 tonight"). When no start can meet it,
deadline_policy decides between BEST_EFFORT (run as early as possible, so
the delay rather than the cost is minimized) and STRICT (keep the
deadline; a ONCE consumer then fails the optimization). The solution
reports appliance_deadline_missed per device.
The EV charging target can be given the same kind of deadline, as an
absolute min_soc_deadline_datetime and/or a relative min_soc_max_duration_h
("full in 6 hours"), the earlier of the two winning. The ev_soc_miss
penalty is then evaluated at that slot instead of at the end of the
horizon, and the seeding heuristic only proposes charge slots before it.
Battery-to-grid export under direct marketing is no longer all-or-nothing:
grid_export_rates configures the selectable export levels as a factor of
the rated discharge power (default [0.25, 0.5, 0.75, 1.0]). Each rate is
its own optimizer state, with the full-power state keeping its previous
index so existing seeds and heuristics are unaffected. The chosen level
per slot is reported in battery_grid_export_factor and as the
GRID_SUPPORT_EXPORT operation factor.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Replace the single hourly "dishwasher" home appliance with a list of
flexible consumers (home_appliances). Each consumer defines its load
either as an explicit power profile (energy-preservingly resampled onto
the optimization slot grid, incl. 15-min and non-integer interval ratios)
or the flat consumption_wh/duration_h fallback, and runs ONCE or DAILY
within its time windows and the optimization horizon.
- ConsumerScheduleMode + shared load-definition validation (XOR of
profile/fallback, reject negative/NaN/inf, unique device_id)
- ApplianceGeneLayout: variable appliance gene block (index into
allowed_start_slots), ONCE/DAILY calendar-day based, no snapping
- per-device output: result.home_appliance_energy_wh, appliance_starts
(absolute local times), per-device solution columns and DDBC RUN/OFF
instructions on state transitions only
- deprecate dishwasher/washingstart/Home_appliance_wh_per_hour with
backward-compatible mapping and explicit conflict rejection
- max_home_appliances is now an upper bound only; no demo appliance and
no on/off behaviour
- docs, openapi.json, CHANGELOG and optimize_result_2* fixtures updated;
new tests/test_homeappliance.py covers the mandatory test matrix
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The genetic optimizer was hard-wired to an hourly grid and forced
optimization.interval to 3600 s. Generalize it to a configurable slot grid
of length prediction.hours * (3600 / interval), accepting 900 (15 min) in
addition to the default 3600 (1 hour) so the optimizer can schedule on a
quarter-hour grid for 15-minute dynamic electricity tariffs.
- genetic.py: slot_duration_h / slots_per_hour / total_slots helpers; all GA
vectors sized by total_slots; simulate()/evaluate() indexed by start slot.
- geneticparams.py: allow {900, 3600}; scale the load power series to per-slot
energy, mirroring the PV series.
- battery.py / inverter.py: scale power caps to per-slot energy caps via
slot_duration_h; homeappliance.py carries the hook.
- geneticsolution.py: serialize solution and plan on the slot grid (interval
freq, start-slot offset, second-based instruction instants).
The default 3600 s interval keeps the previous hourly behaviour; the genetic
regression suite is unchanged. Adds tests for the 15-minute slot grid.
Fügt einen Direktvermarktungs-Modus (feedintariff.direct_marketing_enabled)
hinzu, der den Börsenpreis als Einspeisevergütung nutzt und aktive
Batterie-Entladung ins Netz (battery_grid_export_allowed) sowie
DC-Charge-Bypass optimiert.
- FeedInTariffEnergyCharts-Provider (Börsen-Einspeisetarif inkl. Prognose)
- Inverter: DC/AC-Wirkungsgrade und Batterie-Grid-Export in process_energy
- Genetik: Export-/DC-Charge-Zustände, Restwert-Bewertung des Akkus
- Solution-Result: neues Feld Feed_in_tariff (verwendeter Tarif je Stunde)
- Tests für neue Provider, Solution und Simulation
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* 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>
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
Pydantic deprecates using extra keyword arguments on Field.
Used json_schema_extra instead.
Deprecated in Pydantic V2.0 to be removed in V3.0.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
* 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>
Allow charge rates for electric vehicle to be provided by the POST
optimize endpoint. Create a default value in case neither the
parameters nor the configuration provide charge rates.
This is also to allow to migrate from 0.1.0 configuration format
to actual one.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This fix implements the long term goal to have the EOS server run optimization (or
energy management) on regular intervals automatically. Thus clients can request
the current energy management plan at any time and it is updated on regular
intervals without interaction by the client.
This fix started out to "only" make automatic optimization (or energy management)
runs working. It turned out there are several endpoints that in some way
update predictions or run the optimization. To lock against such concurrent attempts
the code had to be refactored to allow control of execution. During refactoring it
became clear that some classes and files are named without a proper reference
to their usage. Thus not only refactoring but also renaming became necessary.
The names are still not the best, but I hope they are more intuitive.
The fix includes several bug fixes that are not directly related to the automatic optimization
but are necessary to keep EOS running properly to do the automatic optimization and
to test and document the changes.
This is a breaking change as the configuration structure changed once again and
the server API was also enhanced and streamlined. The server API that is used by
Andreas and Jörg in their videos has not changed.
* fix: automatic optimization
Allow optimization to automatically run on configured intervals gathering all
optimization parameters from configuration and predictions. The automatic run
can be configured to only run prediction updates skipping the optimization.
Extend documentaion to also cover automatic optimization. Lock automatic runs
against runs initiated by the /optimize or other endpoints. Provide new
endpoints to retrieve the energy management plan and the genetic solution
of the latest automatic optimization run. Offload energy management to thread
pool executor to keep the app more responsive during the CPU heavy optimization
run.
* fix: EOS servers recognize environment variables on startup
Force initialisation of EOS configuration on server startup to assure
all sources of EOS configuration are properly set up and read. Adapt
server tests and configuration tests to also test for environment
variable configuration.
* fix: Remove 0.0.0.0 to localhost translation under Windows
EOS imposed a 0.0.0.0 to localhost translation under Windows for
convenience. This caused some trouble in user configurations. Now, as the
default IP address configuration is 127.0.0.1, the user is responsible
for to set up the correct Windows compliant IP address.
* fix: allow names for hosts additional to IP addresses
* fix: access pydantic model fields by class
Access by instance is deprecated.
* fix: down sampling key_to_array
* fix: make cache clear endpoint clear all cache files
Make /v1/admin/cache/clear clear all cache files. Before it only cleared
expired cache files by default. Add new endpoint /v1/admin/clear-expired
to only clear expired cache files.
* fix: timezonefinder returns Europe/Paris instead of Europe/Berlin
timezonefinder 8.10 got more inaccurate for timezones in europe as there is
a common timezone. Use new package tzfpy instead which is still returning
Europe/Berlin if you are in Germany. tzfpy also claims to be faster than
timezonefinder.
* fix: provider settings configuration
Provider configuration used to be a union holding the settings for several
providers. Pydantic union handling does not always find the correct type
for a provider setting. This led to exceptions in specific configurations.
Now provider settings are explicit comfiguration items for each possible
provider. This is a breaking change as the configuration structure was
changed.
* fix: ClearOutside weather prediction irradiance calculation
Pvlib needs a pandas time index. Convert time index.
* fix: test config file priority
Do not use config_eos fixture as this fixture already creates a config file.
* fix: optimization sample request documentation
Provide all data in documentation of optimization sample request.
* fix: gitlint blocking pip dependency resolution
Replace gitlint by commitizen. Gitlint is not actively maintained anymore.
Gitlint dependencies blocked pip from dependency resolution.
* fix: sync pre-commit config to actual dependency requirements
.pre-commit-config.yaml was out of sync, also requirements-dev.txt.
* fix: missing babel in requirements.txt
Add babel to requirements.txt
* feat: setup default device configuration for automatic optimization
In case the parameters for automatic optimization are not fully defined a
default configuration is setup to allow the automatic energy management
run. The default configuration may help the user to correctly define
the device configuration.
* feat: allow configuration of genetic algorithm parameters
The genetic algorithm parameters for number of individuals, number of
generations, the seed and penalty function parameters are now avaliable
as configuration options.
* feat: allow configuration of home appliance time windows
The time windows a home appliance is allowed to run are now configurable
by the configuration (for /v1 API) and also by the home appliance parameters
(for the classic /optimize API). If there is no such configuration the
time window defaults to optimization hours, which was the standard before
the change. Documentation on how to configure time windows is added.
* feat: standardize mesaurement keys for battery/ ev SoC measurements
The standardized measurement keys to report battery SoC to the device
simulations can now be retrieved from the device configuration as a
read-only config option.
* feat: feed in tariff prediction
Add feed in tarif predictions needed for automatic optimization. The feed in
tariff can be retrieved as fixed feed in tarif or can be imported. Also add
tests for the different feed in tariff providers. Extend documentation to
cover the feed in tariff providers.
* feat: add energy management plan based on S2 standard instructions
EOS can generate an energy management plan as a list of simple instructions.
May be retrieved by the /v1/energy-management/plan endpoint. The instructions
loosely follow the S2 energy management standard.
* feat: make measurement keys configurable by EOS configuration.
The fixed measurement keys are replaced by configurable measurement keys.
* feat: make pendulum DateTime, Date, Duration types usable for pydantic models
Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are
added to the datetimeutil utility. Remove custom made pendulum adaptations
from EOS pydantic module. Make EOS modules use the pydantic pendulum types
managed by the datetimeutil module instead of the core pendulum types.
* feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil.
The time windows are are added to support home appliance time window
configuration. All time classes are also pydantic models. Time is the base
class for time definition derived from pendulum.Time.
* feat: Extend DataRecord by configurable field like data.
Configurable field like data was added to support the configuration of
measurement records.
* feat: Add additional information to health information
Version information is added to the health endpoints of eos and eosDash.
The start time of the last optimization and the latest run time of the energy
management is added to the EOS health information.
* feat: add pydantic merge model tests
* feat: add plan tab to EOSdash
The plan tab displays the current energy management instructions.
* feat: add predictions tab to EOSdash
The predictions tab displays the current predictions.
* feat: add cache management to EOSdash admin tab
The admin tab is extended by a section for cache management. It allows to
clear the cache.
* feat: add about tab to EOSdash
The about tab resembles the former hello tab and provides extra information.
* feat: Adapt changelog and prepare for release management
Release management using commitizen is added. The changelog file is adapted and
teh changelog and a description for release management is added in the
documentation.
* feat(doc): Improve install and devlopment documentation
Provide a more concise installation description in Readme.md and add extra
installation page and development page to documentation.
* chore: Use memory cache for interpolation instead of dict in inverter
Decorate calculate_self_consumption() with @cachemethod_until_update to cache
results in memory during an energy management/ optimization run. Replacement
of dict type caching in inverter is now possible because all optimization
runs are properly locked and the memory cache CacheUntilUpdateStore is properly
cleared at the start of any energy management/ optimization operation.
* chore: refactor genetic
Refactor the genetic algorithm modules for enhanced module structure and better
readability. Removed unnecessary and overcomplex devices singleton. Also
split devices configuration from genetic algorithm parameters to allow further
development independently from genetic algorithm parameter format. Move
charge rates configuration for electric vehicles from optimization to devices
configuration to allow to have different charge rates for different cars in
the future.
* chore: Rename memory cache to CacheEnergyManagementStore
The name better resembles the task of the cache to chache function and method
results for an energy management run. Also the decorator functions are renamed
accordingly: cachemethod_energy_management, cache_energy_management
* chore: use class properties for config/ems/prediction mixin classes
* chore: skip debug logs from mathplotlib
Mathplotlib is very noisy in debug mode.
* chore: automatically sync bokeh js to bokeh python package
bokeh was updated to 3.8.0, make JS CDN automatically follow the package version.
* chore: rename hello.py to about.py
Make hello.py the adapted EOSdash about page.
* chore: remove demo page from EOSdash
As no the plan and prediction pages are working without configuration, the demo
page is no longer necessary
* chore: split test_server.py for system test
Split test_server.py to create explicit test_system.py for system tests.
* chore: move doc utils to generate_config_md.py
The doc utils are only used in scripts/generate_config_md.py. Move it there to
attribute for strong cohesion.
* chore: improve pydantic merge model documentation
* chore: remove pendulum warning from readme
* chore: remove GitHub discussions from contributing documentation
Github discussions is to be replaced by Akkudoktor.net.
* chore(release): bump version to 0.1.0+dev for development
* build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1
bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break.
* build(deps): bump uvicorn from 0.36.0 to 0.37.0
BREAKING CHANGE: EOS configuration changed. V1 API changed.
- The available_charge_rates_percent configuration is removed from optimization.
Use the new charge_rate configuration for the electric vehicle
- Optimization configuration parameter hours renamed to horizon_hours
- Device configuration now has to provide the number of devices and device
properties per device.
- Specific prediction provider configuration to be provided by explicit
configuration item (no union for all providers).
- Measurement keys to be provided as a list.
- New feed in tariff providers have to be configured.
- /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints.
- /v1/admin/cache/clear now clears all cache files. Use
/v1/admin/cache/clear-expired to only clear all expired cache files.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Andreas
ChristinandAndreas
Andreas
Christopher NadlerandGitHub