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* perf(prediction): evaluate self-consumption tables without SciPy The GENETIC inverter (and the GENETIC0 one) evaluates a pickled scipy.interpolate.RegularGridInterpolator for the self-consumption probabilities. SciPy is used nowhere else in EOS, yet loading it costs about 35 MB RSS (21 MB anonymous) on Linux arm64, which every optimization run pays. Add utils/gridinterpolator.py: - LinearGridInterpolator: 2-D linear interpolation with the same steps as SciPy's linear 2-D path (interval search, normalised distances, corner weights summed in the same order, fill value outside the grid, NaN for NaN input). - load_grid_interpolator(): reads the shipped pickles with a restricted unpickler that maps the SciPy class to a stand-in and accepts only the NumPy globals needed for the arrays. SciPy is not imported, and the data files no longer depend on SciPy's private module paths. Results are bit-identical to SciPy on Linux (aarch64, checked on 3.6M points per table including the query pattern of the inverter). The macOS arm64 SciPy wheel fuses multiply-add, so there SciPy itself differs by <= 1.1e-16; GENETIC plans for fixed seeds are unchanged on both platforms. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(prediction): make the SciPy-free table evaluation opt-in Add optimization.self_consumption_interpolator (scipy | numpy, default scipy). The default keeps evaluating the self-consumption tables with SciPy's RegularGridInterpolator, built from the same grid, values and fill value the pickle holds, so results are unchanged. With numpy the tables are evaluated by LinearGridInterpolator and SciPy is never imported. The backend is read on every evaluation, so a config change applies to the next run. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> --------- Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: Andreas <drbacke@gmx.de>
255 lines
10 KiB
Markdown
255 lines
10 KiB
Markdown
## General Optimization Configuration
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<!-- pyml disable line-length -->
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:::{table} optimization
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:widths: 10 20 10 5 5 30
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:align: left
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| Name | Environment Variable | Type | Read-Only | Default | Description |
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| ---- | -------------------- | ---- | --------- | ------- | ----------- |
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| algorithm | `EOS_OPTIMIZATION__ALGORITHM` | `<enum 'OptimizationAlgorithm'>` | `rw` | `required` | Optimization algorithm [GENETIC | GENETIC0]. Defaults to GENETIC. |
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| algorithms | | `list[str]` | `ro` | `N/A` | Available optimization algorithms. |
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| genetic | `EOS_OPTIMIZATION__GENETIC` | `GeneticCommonSettings` | `rw` | `required` | GENETIC optimization algorithm configuration. |
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| genetic0 | `EOS_OPTIMIZATION__GENETIC0` | `Genetic0CommonSettings` | `rw` | `required` | GENETIC0 optimization algorithm configuration. |
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| keys | | `list[str]` | `ro` | `N/A` | The keys of the solution. |
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| self_consumption_interpolator | `EOS_OPTIMIZATION__SELF_CONSUMPTION_INTERPOLATOR` | `<enum 'GridInterpolatorBackend'>` | `rw` | `scipy` | How the inverter's self-consumption probability table is evaluated [scipy | numpy]. 'scipy' (default) uses scipy.interpolate. 'numpy' gives the same values without importing SciPy, which saves about 35 MB RSS after the first optimization run (useful on devices with little RAM); a run takes about 2.5 % longer. |
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:::
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<!-- pyml disable no-emphasis-as-heading -->
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**Example Input**
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```json
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{
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"optimization": {
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"algorithm": "GENETIC",
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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}
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},
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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}
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},
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"self_consumption_interpolator": "scipy"
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}
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}
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```
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**Example Output**
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<!-- pyml disable line-length -->
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```json
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{
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"optimization": {
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"algorithm": "GENETIC",
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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},
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"horizon": 24
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},
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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},
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"interval_sec": 3600,
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"horizon": 24
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},
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"self_consumption_interpolator": "scipy",
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"algorithms": [
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"GENETIC",
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"GENETIC0"
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],
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"keys": []
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}
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}
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```
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<!-- pyml enable line-length -->
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### GENETIC0 Optimization Algorithm Configuration
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:::{table} optimization::genetic0
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:widths: 10 10 5 5 30
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:align: left
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| Name | Type | Read-Only | Default | Description |
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| ---- | ---- | --------- | ------- | ----------- |
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| generations | `Optional[int]` | `rw` | `400` | Number of generations to evolve [>= 10]. Defaults to 400. |
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| horizon | `int` | `ro` | `N/A` | Number of optimization steps. |
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| horizon_hours | `int` | `rw` | `24` | The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours. |
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| individuals | `Optional[int]` | `rw` | `300` | Number of individuals (solutions) in the population [>= 10]. Defaults to 300. |
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| interval_sec | `int` | `ro` | `N/A` | The optimization interval [sec]. Fixed to 1 hour (3600 seconds). |
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| penalties | `dict[str, Union[float, int, str]]` | `rw` | `required` | Penalty parameters used in fitness evaluation. |
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| seed | `Optional[int]` | `rw` | `None` | Random seed for reproducibility. None = random. |
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:::
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**Example Input**
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<!-- pyml enable no-emphasis-as-heading -->
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<!-- pyml disable line-length -->
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```json
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{
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"optimization": {
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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}
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}
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}
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}
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```
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<!-- pyml enable line-length -->
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**Example Output**
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<!-- pyml disable line-length -->
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```json
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{
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"optimization": {
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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},
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"interval_sec": 3600,
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"horizon": 24
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}
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}
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}
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```
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### GENETIC Optimization Algorithm Configuration
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:::{table} optimization::genetic
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:widths: 10 10 5 5 30
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:align: left
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| Name | Type | Read-Only | Default | Description |
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| ---- | ---- | --------- | ------- | ----------- |
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| fitness_cache_max_entries | `Optional[int]` | `rw` | `None` | Maximum number of keys in the per-run fitness cache. None = unbounded (default), 0 = cache disabled, N > 0 = keep at most N keys and evict the oldest first. Every cached evaluation takes one or two keys. The cache only saves repeated evaluations; the result for a fixed seed is the same with any value. Limit it to cap memory on small devices. |
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| generations | `Optional[int]` | `rw` | `400` | Number of generations to evolve [>= 10]. Defaults to 400. |
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| horizon | `int` | `ro` | `N/A` | Number of optimization steps. |
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| horizon_hours | `int` | `rw` | `24` | The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours. |
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| individuals | `Optional[int]` | `rw` | `300` | Number of individuals (solutions) in the population [>= 10]. Defaults to 300. |
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| interval_sec | `Literal[900, 3600]` | `rw` | `3600` | The optimization interval [sec]. Defaults to 3600 seconds (1 hour) |
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| measurement_max_age_seconds | `int` | `rw` | `300` | Maximum age of SoC measurements for configuration-based optimization [s]. |
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| penalties | `dict[str, Union[float, int, str]]` | `rw` | `required` | Penalty parameters used in fitness evaluation. |
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| seed | `Optional[int]` | `rw` | `None` | Random seed for reproducibility. None = random. |
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| tail_horizon_hours | `int` | `rw` | `48` | Forecast lookahead after the control horizon [h]. No tail commands are issued. Set 0 to disable. |
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| terminal_value_euro_per_kwh | `float` | `rw` | `0.0` | Value assigned to usable battery energy remaining at the end of the optimization horizon [EUR/kWh]. This terminal value is independent of the battery LCOS. Only used with terminal_value_mode = FIXED. Defaults to 0 EUR/kWh. |
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| terminal_value_mode | `<enum 'TerminalValueMode'>` | `rw` | `AUTO` | How to value the energy left in the battery at the end of the control horizon. AUTO solves the forecast tail with an AUTO continuation proxy at its end (or only the proxy if tail is zero); FIXED uses 'terminal_value_euro_per_kwh'. Defaults to AUTO. |
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| terminal_value_window_hours | `int` | `rw` | `24` | Length of the trailing window at the effective tail end the AUTO continuation curve is derived from [h]. One day covers a full load and PV cycle. Defaults to 24 hours. |
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:::
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<!-- pyml enable line-length -->
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<!-- pyml disable no-emphasis-as-heading -->
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**Example Input**
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<!-- pyml enable no-emphasis-as-heading -->
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<!-- pyml disable line-length -->
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```json
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{
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"optimization": {
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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}
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}
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}
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}
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```
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<!-- pyml enable line-length -->
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<!-- pyml disable no-emphasis-as-heading -->
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**Example Output**
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```json
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{
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"optimization": {
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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},
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"horizon": 24
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}
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}
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}
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```
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