Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq

• Published in: Energies (Basel) Vol. 16; no. 6; p. 2919
• Main Authors: AL-Qaysi, Ahmed Mazin Majid, Bozkurt, Altug, Ates, Yavuz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Accuracy; adaptive neuro-based fuzzy inference system; Artificial intelligence; artificial neural network; Back propagation; electrical load forecasting; Energy management systems; Forecasting; Fuzzy logic; Genetic algorithms; Humidity; Machine learning; Methods; Neural networks; Propagation; Variables; Iraq
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16062919

This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system.