How to Evaluate Forecasts for the Energy Sector in 2025 ⚡📊¶

Forecasting is a cornerstone of the energy sector, enabling decision-making for operations, investments, and policy development. However, the real value of a forecast lies in its evaluation and cross-validation. How well did it predict reality? Here’s a step-by-step guide to evaluating forecasts, focusing on understanding the process, selecting the right forecasts, and comparing them against observations.

1. Understand the Process: Decision-Making Context 📅🧠¶

Before diving into evaluation, it’s crucial to understand why the forecast was made and what kind of forecast horizon we need to target this. This context is key to interpreting the results correctly. In general you can differ between three different kind of forecast in the energy industry overall:

Operational Forecasts: Short-term, used for managing daily grid operations or scheduling energy dispatch.
Market Forecasts: Medium-term, help with bidding strategies or setting tariffs.
Strategic Forecasts: Long-term, guide investment decisions like building renewable plants or expanding infrastructure.

By clarifying the decision-making context and forecast horizon upfront, you can set the right expectations for evaluation.

2. Select Forecasts for Evaluation: What and When? 📂¶

Energy systems generate forecasts at multiple time steps and for various variables (e.g., demand, generation, prices). Evaluating them all can be overwhelming, so filter relevant forecasts based on:

Variable of Interest: E.g., electricity demand, wind power, spot prices.
Horizon & Timing: Only forecasts relevant to your decision-making window should be considered.
Availability of Observations: You need actual data (ground truth) to evaluate forecasts.

Example: Selecting Forecasts to Evaluate¶

# Filter forecasts for evaluation
forecasts = pd.DataFrame({
    "calculation_timestamp": ["2024-11-17", "2024-11-17", "2024-11-17"],
    "timestamp": ["2024-11-17", "2024-11-18", "2024-11-19"],
    "forecasted_demand": [1000, 1050, 1100],
    "observed_demand": [1020, 1040, 1095]
})

# Focus on dates within the day-ahead horizon, according to the given calculation timestamp at 17th of novembre: 
start_date = "2024-11-18"
end_date = "2024-11-19"

filtered_forecasts = forecasts[
    (forecasts["timestamp"] >= start_date) & (forecasts["timestamp"] <= end_date)
]

print(filtered_forecasts)

This step ensures you’re only working with forecasts that are meaningful and can be tested against real data.

3. Evaluate Forecasts Against Observations: Metrics & Tools 📈📉 📊¶

With the relevant forecasts in hand, the next step is to calculate forecast accuracy or error metrics by comparing predictions to actual observations. Common metrics include:

Mean Absolute Error (MAE): Average absolute difference between forecasts and observations. Should be used with a normalization only, otherwise it will overweight low valued errors.
Root Mean Square Error (RMSE): Emphasizes larger errors, useful for operational reliability. This is the most powerful metric
Bias: Average over- or under-prediction. Really helpful to understand e.g. optimisation potential, as systematic errors are much easier to vanish. Your forecast should be bias free in any case.

Example: Calculating Error Metrics¶

import numpy as np

# Calculate error metrics
filtered_forecasts["error"] = (
    filtered_forecasts["forecasted_demand"] - filtered_forecasts["observed_demand"]
)

# Metrics
mae = np.mean(abs(filtered_forecasts["error"]))
rmse = np.sqrt(np.mean(filtered_forecasts["error"]**2))
mape = np.mean(abs(filtered_forecasts["error"] / filtered_forecasts["observed_demand"])) * 100

print(f"MAE: {mae:.2f}, RMSE: {rmse:.2f}, MAPE: {mape:.2f}%")

Output:

MAE: 10.00, RMSE: 10.44, MAPE: 0.94%

These metrics quantify how close the forecasts were to the observed values and highlight areas for improvement.

Forecast evaluation is not a one-and-done task. Use insights from evaluation to refine your models or forecasting processes:

Investigate Bias: If forecasts are consistently over- or under-predicting, revisit assumptions or inputs.
Tailor Horizons: Maybe different horizons need distinct models.
Collaborate: Feedback loops between forecasters and decision-makers can ensure alignment.

Tools for Forecast Evaluation 🛠¶

Python libraries like pandas, numpy, and scikit-learn are great for quick evaluations. For advanced needs, explore:

statsmodels: Time series analysis and evaluation. Test stationarity and seasonality.
forecast-tools: Pre-built utilities for energy forecasting.
matplotlib or seaborn: Visualization to uncover patterns or anomalies.

🏁 Conclusion 🏁¶

Evaluating energy sector forecasts isn’t just about crunching numbers—it’s about understanding the decision context, selecting relevant forecasts, and applying the right metrics. By following a structured approach, you can ensure forecasts serve their ultimate purpose: empowering smart decisions. 🎯✨

Got any tips or favorite metrics? Share them in the comments below! 👇