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