kbt-devops
fe76bc7629
Implements FastAPI backend with ML model support for energy trading, including price prediction models and RL-based battery trading policy. Features dashboard, trading, backtest, and settings API routes with WebSocket support for real-time updates.
36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import pandas as pd
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def add_battery_features(
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df: pd.DataFrame,
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price_df: pd.DataFrame,
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battery_col: str = "charge_level_mwh",
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capacity_col: str = "capacity_mwh",
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timestamp_col: str = "timestamp",
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battery_id_col: str = "battery_id",
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) -> pd.DataFrame:
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result = df.copy()
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if battery_col in result.columns and capacity_col in result.columns:
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result["charge_level_pct"] = result[battery_col] / result[capacity_col]
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result["discharge_potential_mwh"] = result[battery_col] * result.get("efficiency", 0.9)
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result["charge_capacity_mwh"] = result[capacity_col] - result[battery_col]
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if price_df is not None and "real_time_price" in price_df.columns and timestamp_col in result.columns:
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merged = result.merge(
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price_df[[timestamp_col, "real_time_price"]],
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on=timestamp_col,
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how="left",
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suffixes=("", "_market")
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)
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if "real_time_price_market" in merged.columns:
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result["market_price"] = merged["real_time_price_market"]
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result["charge_cost_potential"] = result["charge_capacity_mwh"] * result["market_price"]
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result["discharge_revenue_potential"] = result["discharge_potential_mwh"] * result["market_price"]
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return result
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__all__ = ["add_battery_features"]
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