Numerical Optimization of Neuro-Fuzzy Models Using Evolutionary Algorithms for Electricity Demand Forecasting in Pre-Tertiary Institutions

Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, the impact of two clustering techniques, Subtractive Clustering (SC) and Fuzzy C-Means (FCM), along with their cogent hyperparameters, were investigated, yielding several sub-models. The efficacy of the proposed models was evaluated using five standard statistical metrics, while the optimal model was also compared with other variants and hybrid models. Results obtained showed that the GA-ANFIS-FCM with four clusters achieved the best performance, recording the lowest Root Mean Square Error (RMSE) of 3.83, Mean Absolute Error (MAE) of 2.40, Theil’s U of 0.87, and Standard Deviation (SD) of 3.82. The developed model contributes valuable insights towards informed energy decisions.