Time Series Forecasting of Energy Demand Using Machine and Deep Learning Approach

• Author(s): Raphael Ibraimoh ; Oluwakemi Shojupe
• Paper ID: 1706377
• Page: 115-127
• Published Date: 09-10-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 4 October-2024

Abstract

This study analyzes the application of machine learning (ML) and deep learning (DL) models to forecast hourly national energy consumption. As energy demand grows increasingly complex, accurate forecasting is crucial for maintaining grid stability, optimizing costs, and ensuring efficient resource management. The study evaluates various models, including Random Forest, XGBoost, Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN), as well as a hybrid model combining TCN with XGBoost. XGBoost had the best performance, achieving a Root Mean Squared Error (RMSE) of 393.48 and a Mean Absolute Percentage Error (MAPE) of 1.16%. The research also employed SHAP (SHapley Additive exPlanations) to interpret the model's decision-making process, highlighting the significance of features like lagged demand and temporal elements (e.g., hour of the day). These insights confirm the model's reliability for short-term energy forecasting, offering valuable tools for energy providers and operators. Using the XGBoost model, a forecast for the upcoming week was generated, demonstrating the model’s ability to maintain accuracy in short-term predictions. Residual analysis indicates the model's predictions are unbiased, further supporting its operational reliability.

Keywords

Energy Demand Forecasting, Machine Learning, Deep Learning, XGBoost, LSTM, TCN, SHAP Analysis, Time Series Analysis

Citations

IRE Journals:
Raphael Ibraimoh , Oluwakemi Shojupe "Time Series Forecasting of Energy Demand Using Machine and Deep Learning Approach" Iconic Research And Engineering Journals Volume 8 Issue 4 2024 Page 115-127

IEEE:
Raphael Ibraimoh , Oluwakemi Shojupe "Time Series Forecasting of Energy Demand Using Machine and Deep Learning Approach" Iconic Research And Engineering Journals, 8(4)