perf(prediction): optional SciPy-free evaluation of the self-consumption tables (#1378)

* 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>

docs/_generated/configexample.md

@@ -276,7 +276,8 @@
                "penalties": {
                    "ev_soc_miss": 10
                }
-           }
+           },
+           "self_consumption_interpolator": "scipy"
        },
        "prediction": {
            "hours": 48,

docs/_generated/configoptimization.md

@@ -12,6 +12,7 @@
 | genetic | `EOS_OPTIMIZATION__GENETIC` | `GeneticCommonSettings` | `rw` | `required` | GENETIC optimization algorithm configuration. |
 | genetic0 | `EOS_OPTIMIZATION__GENETIC0` | `Genetic0CommonSettings` | `rw` | `required` | GENETIC0 optimization algorithm configuration. |
 | keys | | `list[str]` | `ro` | `N/A` | The keys of the solution. |
+| 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. |
 :::
 <!-- pyml enable line-length -->
 
@@ -48,7 +49,8 @@
                "penalties": {
                    "ev_soc_miss": 10
                }
-           }
+           },
+           "self_consumption_interpolator": "scipy"
        }
    }
 ```
@@ -91,6 +93,7 @@
                "interval_sec": 3600,
                "horizon": 24
            },
+           "self_consumption_interpolator": "scipy",
            "algorithms": [
                "GENETIC",
                "GENETIC0"

docs/_generated/openapi.md

@@ -1,6 +1,6 @@
 # Akkudoktor-EOS
 
-**Version**: `v0.3.0.dev2609171762020752`
+**Version**: `v0.3.0.dev2610021392659061`
 
 <!-- pyml disable line-length -->
 **Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.