Journal of Energy Technology and Environment 4(4) 2022 pp.28- 34 

Comparative Study of Load Demand Forecast Using Non-Linear Regression and Neural Network Techniques: A Case Study

Edohen O.M . and Idubor S.O.

Abstract

Demand load forecasting is the estimation of electrical load that will be required by a certain geographical area with the use of previous electrical load data in the said geographical area. This has become necessary due to the increasing number of prospective power users around the world to address for likely shortage of electricity and to further plan for resources budgeting and power real time availability. However, this research work carried out a short term comparative study of electric load demand forecast of the University of Benin, Ugbowo campus between 1st to 30th September, 2019. The forecasting approaches used in realizing this task are the non-linear regression and artificial neural network (ANN) approaches which was analyzed using MATLAB 2015 software. The current load demand was presented and modeled using the two approaches, the ANN gave an optimal result of 0.0021% mean absolute percentage error (MAPE) for all the days and all the constraint parameters used for the training of the model. Following the validation of the ANN model with the non- linear regression (NLR) model, it was observed that the artificial neural network gave the best optimal result of 0.0021% MAPE for all constraints applied while the non-linear regression model gave an optimal result of 0.0448% MAPE. Therefore, the artificial neural network (ANN) model is considered to produce a more accurate result than the non-linear regression model as confirmed by the result of validation clearly confirming model suitability for the analysis.

Keywords: Non-ilinear regression (NLR), artificial neural network (ANN), forecasting, power load demand, Mean Absolute Percentage Error (MAPE), performance ratio

Cite this article as: Edohen O.M and Idubor S.O. (2022). Comparative Study of Load Demand Forecast Using Non-Linear Regression and Neural Network Techniques: A case Study. Journal of Energy Technology and Environment (NIPES), 4(4), 28–34. https://doi.org/10.5281/zenodo.7445770