Enhanced adaptive neuro-fuzzy inference system using genetic algorithm: a case study in predicting electricity consumption

Abstract

Abstract Energy forecasting is crucial for efficient energy management and planning for future energy needs. Previous studies have employed hybrid modeling techniques, but insufficient attention has been given to hyper-parameter tuning and parameter selection. In this study, we present a hybrid model, which combines fuzzy c-means clustered adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), named GA–ANFIS–FCM, to model electricity consumption in Lagos districts, Nigeria. The model is simulated using the algorithms’ control settings, and the best model is identified after assessing their performance using renowned statistical indicators. To further narrow down the best viable model, the impact of the core parameter of the GA on the GA–ANFIS–FCM optimal model is examined by varying the crossover percentage in the range of 0.2–0.6. Firstly, the results reveal the better performance of the hybridized ANFIS model than the standalone ANFIS model. Additionally, the best model is obtained with the GA–ANFIS–FCM model with four clusters at a crossover percentage of 0.4, with mean absolute percentage error (MAPE), mean absolute error (MAE), coefficient of root mean square error (CVRMSE), root mean square error (RMSE) values of 7.6345 (signifying a forecast accuracy of 92.4%), 706.0547, 9.4913, and 918.6518 during the testing phase, respectively. The study demonstrates the potential of the proposed model as a reliable tool for energy forecasting.

Keywords

• Models
• Machine learning
• Adaptive neuro-fuzzy inference system
• Fuzzy c-means clustered