ryukyi / energy-demand-forecast

AB testing sample forecast data

Energy Price Forecasting with Weather Data: A/B Testing Approach

This document outlines a structured approach to conducting A/B testing to explore the potential enhancement of energy price forecasting models through the integration of weather data. The objective is to ascertain whether the inclusion of weather data features leads to more accurate and reliable forecasts.

Table of Contents

• Energy Price Forecasting with Weather Data: A/B Testing Approach
  • Table of Contents
  • 1. Hypothesis
  • 2. Data Collection
  • 3. Data Preprocessing
  • 4. Model Development
  • 5. Model Evaluation
  • 6. Statistical Testing
  • 7. Conclusion and Next Steps

1. Hypothesis

• Null Hypothesis (H0): The integration of weather data does not improve the accuracy of energy price forecasting.
• Alternative Hypothesis (H1): The inclusion of weather data significantly enhances the predictive accuracy of energy price forecasting models.

2. Data Collection

• Energy Prices: Gather historical energy price data.
• Weather Data: Collect corresponding weather data (e.g., temperature, humidity, wind speed).

3. Data Preprocessing

• Clean Data: Ensure empties are addressed and data is indexed
• Normalize Data: Ensure both datasets are on a similar scale.
• Feature Engineering: Create features from the weather data that might influence energy prices (e.g., average temperature, rainfall).

4. Model Development

• Model A (Control): Develop a forecasting model using only historical energy price data.
• Model B (Experiment): Develop a forecasting model using both historical energy price data and the weather data features.

5. Model Evaluation

• Metrics: Choose appropriate metrics for evaluation (e.g., Mean Absolute Error, Root Mean Squared Error).
• Comparison: Compare the performance of Model A and Model B on the test set.

6. Statistical Testing

For time series forecasting, the choice of statistical tests should account for the temporal nature of the data.

• Autocorrelation Tests: Use tests like the Durbin-Watson test to check for autocorrelation in the residuals of the models.
• Granger Causality Test: Assess if the weather data Granger-causes the energy prices, indicating a predictive relationship.
• Ljung-Box Test: Test the residuals of the models for independence, ensuring the models are adequately specified.

7. Conclusion and Next Steps

• Insights: Based on the results, draw conclusions about the potential benefits of incorporating weather data into energy price forecasting.
• Decision: Decide whether to further explore the integration of weather data into forecasting models based on the significance of the results.