Prediction of Electrical Energy Consumption in University Campus Residence Using FCM-Clustered Neuro-Fuzzy Model

Abstract

Developing a viable data-driven policy for the management of electrical-energy consumption in campus residences is contingent on the proper knowledge of the electricity usage pattern and its predictability. In this study, an adaptive neuro-fuzzy inference systems (ANFIS) was developed to model the electrical energy consumption of students’ residence using the University of Johannesburg, South Africa as a case study. The model was developed based on the environmental conditions vis-à-vis meteorological parameters namely temperature, wind speed, and humidity of the respective days as the input variables while electricity consumption (kWh) was used as the output variable. The fuzzy c-means (FCM) is a type of clustering technique that is preferred owing to its speed boost capacity. The best FCM-clustered ANFIS-model based on a range of 2–10 clusters was selected after evaluating their performance using relevant statistical metrics namely; mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute deviation (MAD). FCM-ANFIS with 7 clusters outperformed all other models with the least error and highest accuracy. The RMSE, MAPE, MAD, and R2-values of the best models are 0.043, 0.65, 1.051, and 0.9890 respectively. The developed model will assist in optimizing energy consumption and assist in designing and sizing alternative energy systems for campus residences.