Abdullateef, A. ISalami, M.J.ETijani, I.BAjala, M.T2021-06-032021-06-032019Abdullateef, A. I., Salami, M. E., Tijani, I. B., & Ajala, M. T. (2019): Modified Narx Network for Low Voltage Consumer Load Prediction. Zaria Journal of Electrical Engineering Technology, 8(2), 24–32, Published by Department of Electrical and Electronics Engineering, Amadu Bello University Zaria. Available online at https://www.zjeet.ng/journal-more-details.php?token=modified-narx-network-for-low-voltage-consumer-load-predictionhttps://uilspace.unilorin.edu.ng/handle/20.500.12484/5860This paper is a continuation of our previous work on Nonlinear autoregressive with eXogenous input (NARX) for load prediction. Application of NARX network in real-time might be difficult since the tapped delay was selected by trial and error, leading to nine hidden neurons which makes the network complex. This NARX network was modified based on Genetic Algorithm (GA) and Differential Evolution (DE) resulting in a new model coded NARX-DE-GA. GA and DE search for the number of hidden neurons and tapped delay automatically. The NARX-DE-GA was used to predict the consumer load using one month energy data with 8928 data points. The results show that NARX- DE -GA outperformed the NARX network. The training mean square error (MSE) value for NARX- DE -GA is 0.0253 while validation is 0.0612. These values are slightly higher when compared with NARX network in previous study which are 0.0225 and 0.0533 respectively. However, the network structure which is one input and output tapped delay, and one hidden neuron is simple and applicable in real timeConsumer load predictionData acquisitionDifferential geneticAlgorithm evolutionNonlinear autoregressive with eXogenous inputArticle