Basic framework for simulation evaluation.

Author: Jan Pecinovsky

openenergyid/const.py

@@ -7,6 +7,7 @@
 ELECTRICITY_DELIVERED: Literal["electricity_delivered"] = "electricity_delivered"
 ELECTRICITY_EXPORTED: Literal["electricity_exported"] = "electricity_exported"
 ELECTRICITY_PRODUCED: Literal["electricity_produced"] = "electricity_produced"
+ELECTRICITY_CONSUMED: Literal["electricity_consumed"] = "electricity_consumed"
 
 PRICE_DAY_AHEAD: Literal["price_day_ahead"] = "price_day_ahead"
 PRICE_IMBALANCE_UPWARD: Literal["price_imbalance_upward"] = "price_imbalance_upward"

openenergyid/simeval/__init__.py

@@ -0,0 +1,6 @@
+"""Module containing basic evaluation functions for energy systems."""
+
+from .main import evaluate
+from .models import EvaluationInput
+
+__all__ = ["EvaluationInput", "evaluate"]

openenergyid/simeval/main.py

@@ -0,0 +1,63 @@
+"""Module for evaluating energy simulation data."""
+
+import pandas as pd
+
+from .. import const
+
+
+def evaluate(
+    data: pd.DataFrame, return_frequencies: list[str] | None = None
+) -> dict[str, pd.DataFrame]:
+    """Evaluate the data and return resampled results.
+
+    Args:
+        data: A pandas DataFrame containing time series data with columns:
+            - electricity_delivered
+            - electricity_exported
+            - electricity_produced
+        return_frequencies: List of pandas offset aliases for resampling frequencies.
+            Defaults to ['MS'] (Month Start) if None.
+
+    Returns:
+        A dictionary with keys as frequencies and values as resampled DataFrames.
+    """
+    evaluator = Evaluator(data=data, return_frequencies=return_frequencies)
+    return evaluator.evaluate()
+
+
+class Evaluator:
+    """Evaluator for basic energy system evaluation."""
+
+    def __init__(self, data: pd.DataFrame, return_frequencies: list[str] | None = None):
+        """Initialize the evaluator with data and return frequencies."""
+        self.data = data
+        if return_frequencies is None:
+            self.return_frequencies = ["MS"]  # Month Start
+        else:
+            self.return_frequencies = return_frequencies
+
+    def evaluate(self) -> dict[str, pd.DataFrame]:
+        """Evaluate the data and return resampled results."""
+        # Add electricy_consumed
+        self.data[const.ELECTRICITY_CONSUMED] = (
+            self.data[const.ELECTRICITY_DELIVERED]
+            - self.data[const.ELECTRICITY_EXPORTED]
+            + self.data[const.ELECTRICITY_PRODUCED]
+        )
+
+        results = {}
+        for freq in self.return_frequencies:
+            resampled = (
+                self.data[
+                    [
+                        const.ELECTRICITY_DELIVERED,
+                        const.ELECTRICITY_EXPORTED,
+                        const.ELECTRICITY_PRODUCED,
+                        const.ELECTRICITY_CONSUMED,
+                    ]
+                ]
+                .resample(freq)
+                .sum()
+            )
+            results[freq] = resampled
+        return results

openenergyid/simeval/models.py

@@ -0,0 +1,22 @@
+"""Models for basic energy system evaluation."""
+
+from typing import Literal
+
+from pydantic import Field, confloat, conlist
+
+from ..models import TimeDataFrame
+
+
+class EvaluationInput(TimeDataFrame):
+    """Input frame for basic energy system evaluation."""
+
+    columns: list[
+        Literal[
+            "electricity_delivered",
+            "electricity_exported",
+            "electricity_produced",
+            "price_electricity_delivered",
+            "price_electricity_exported",
+        ]
+    ] = Field(min_length=1, max_length=5)
+    data: list[conlist(item_type=confloat(allow_inf_nan=True), min_length=1, max_length=5)]  # type: ignore