A hybrid statistical and machine learning based forecasting framework for the energy sector.

Area of Science:

• Economics
• Data Science
• Energy Policy

Background:

• Energy prices exhibit high volatility due to supply-demand dynamics, policy shifts, environmental goals, and technological advancements.
• Accurate energy price forecasting is vital for energy security, sustainability, and affordability, influencing policy design and societal progress.
• Existing forecasting methods lack consistent superiority, necessitating advanced frameworks.

Purpose of the Study:

• To introduce the Energy Price Index (EPIC) framework with enhanced forecasting capabilities.
• To forecast prices for 56 diverse energy products using 33 unique time series.
• To evaluate the performance of various statistical and machine learning methods for energy price prediction.

Main Methods:

• Developed the Energy Price Index (EPIC) framework integrating statistical and machine learning forecasting techniques.
• Applied the framework to analyze historical price data for 56 energy products across 33 time series.
• Incorporated analysis of seasonality, trends, and outliers for each energy product's historical prices.
• Enabled energy price forecasting up to 14 months in advance.

Main Results:

• Deep neural networks demonstrated superior performance compared to traditional statistical forecasting methods.
• The EPIC framework successfully forecasted energy prices for a wide range of products.
• Optimal tuning of neural network hyperparameters was identified as a critical factor for achieving high accuracy.

Conclusions:

• The EPIC framework offers advanced capabilities for energy price forecasting.
• Deep learning models, particularly neural networks with proper hyperparameter tuning, are highly effective for energy price prediction.
• Accurate forecasting is essential for navigating energy market volatility and supporting policy objectives.