_____________________________________________________________________________________________________ *Corresponding author: Email: atemilola@gmail.com, kayoluwaseyi@gmail.com; Journal of Engineering Research and Reports 20(6): 75-87, 2021; Article no.JERR.67557 ISSN: 2582-2926 Hybrid Based Artificial Intelligence Short –Term Load Forecasting Kayode O. Adebunmi 1* , Temilola M. Adepoju 2* , Gafari A. Adepoju 1 and Akeem O. Bisiriyu 3 1 Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, Nigeria. 2 Department of Computer Engineering, Federal Polytechnic Ede, Nigeria. 3 Department of Electronic and Electrical Engineering, The Polytechnic Iree, Nigeria. Authors’ contributions This work was carried out in collaboration among the authors. All authors read, reviewed and approved the final manuscript. Article Information DOI: 10.9734/JERR/2021/v20i617330 Editor(s): (1) Dr. Guang Yih Sheu, Chang-Jung Christian University, Taiwan. Reviewers: (1) Siti Zulaiha Ahmad, Universiti Teknologi MARA (UiTM), Malaysia. (2) Abdelhakim El hendouzi, Mohammed V University, Morocco. Complete Peer review History: http://www.sdiarticle4.com/review-history/67557 Received 01 March 2021 Accepted 05 May 2021 Published 10 May 2021 ABSTRACT Electrical power load forecasting, which forms a key element in the power industry's electricity preparation, is used for providing required data for day-to-day system management activities and power utility unit participation. Since the statistical method is a linear model, and the load and meteorological parameters have a nonlinear relationship, the statistical method for load forecasting involves a great calculation time for parameter recognition. Using this tool for load forecasting often results in a major mistake in prediction. Due to the disadvantages of the statistical method of load forecasting Neuro-fuzzy model was used in this work. Three models: Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Multilinear Regression (MLR) were simulated in MATLAB environment and their output results were compared using root mean square error (RMSE) and mean absolute error (MAE). The ANFIS model outperforms the other models with least errors of RMSE and MAE of 2.2198 % and 1.7932 % respectively. Keywords: Load forecasting; electrical load; electricity; neuro-fuzzy model; and artificial intelligence. Original Research Article