Enhancing Residential Electricity Consumption Forecasting with Meta-Heuristic Algorithms - Milad Mohebbi; Behnam Sobhani (Advances in Engineering and Intelligence Systems)
The growing global population has significantly increased energy demand, particularly in the residential building sector. This surge underscores the necessity for accurate energy consumption forecasting to facilitate effective planning and future demand projections. However, traditional methods such as regression face challenges in modeling household electricity consumption due to seasonal and monthly variations. Smart grid technology now allows users to manage home energy use more efficiently and effectively. This study explores optimizing Artificial Neural Network (ANN) parameters using meta-heuristic algorithms instead of traditional gradient-based methods to predict residential electricity consumption across different seasons. The experimental data to train a Radial Basis Function (RBF) neural network were utilized. The meta-heuristic algorithms employed for fine-tuning the ANN's weight and bias parameters include the Genetic Algorithm (GA), Multi-Verse Optimizer (MVO), Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Advanced Grey Wolf Optimizer (AGWO), Biogeography-Based Optimization (BBO), and Particle Swarm Optimization with Grey Wolf Optimizer (PSOGWO). These algorithms were evaluated for their effectiveness in adapting to seasonal variations in electricity consumption data. The results revealed that the PSOGWO algorithm consistently outperformed the others across all seasons, particularly in spring and summer. Statistical measures indicated superior accuracy and reliability in predicting energy usage. Specifically, the nine-neuron configuration with the PSOGWO algorithm achieved a high R² value of 0.99077, reflecting lower error metrics. Conclusively, the PSOGWO model's consistent performance across seasonal variations underscores its potential for reliable residential electricity consumption forecasting, making it a valuable tool for energy management in smart grids.