Research Article
Optimization of ANFIS Model for Improved Short-term Electrical Load Forecasting
Osita Omeje
,
Favour Edwards,
Linus Idoko*
Issue:
Volume 15, Issue 3, June 2026
Pages:
34-44
Received:
12 May 2026
Accepted:
25 May 2026
Published:
2 June 2026
DOI:
10.11648/j.epes.20261503.11
Downloads:
Views:
Abstract: For power systems to be stable and reliable, an accurate prediction of electrical load demand is crucial. Electric utilities rely on short-term load forecasting to effectively manage the generation, transmission, and distribution of power to satisfy customer demand. Artificial Neuro-Fuzzy Inference System (ANFIS) is utilized in this work for short-term load forecasting due to its propensity to manage non-linear relationships and uncertainty. ANFIS method has been used in the past for short-term load forecasting, there are certain conditions and issues that need to be resolved. Data normalization, choice of optimization technique, and choice of membership function can greatly influence short-term load forecast using ANFIS. Thus, this paper explored the different choices available and recommended the best choices based on the results obtained. Hourly electrical load data from 24th November 2021 to 4th January 2022, sourced from University of Lagos Power Station, and relevant temperature data within the same range sourced from National Solar Irradiation Database (NSRDB) were used to train and test the various ANFIS architectures. Different ANFIS models, including different membership functions and sets of input data, were simulated on MATLAB, and the performance of the models was evaluated using standard matrices such as root mean square error (RMSE) and mean absolute percentage error (MAPE). The result of this study showed that the inclusion of temperature as an exogenous variable and the use of Gaussian membership function yielded a higher forecast accuracy with MAPE 0.05754% and RMSE 0.56614. This implies that using temperature as an input variable and Gaussian membership functions can improve forecasting accuracy.
Abstract: For power systems to be stable and reliable, an accurate prediction of electrical load demand is crucial. Electric utilities rely on short-term load forecasting to effectively manage the generation, transmission, and distribution of power to satisfy customer demand. Artificial Neuro-Fuzzy Inference System (ANFIS) is utilized in this work for short-...
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