Enhancing Energy Demand Prediction Using Elman Neural Network and Support Vector Machine Model: A Case Study in Lagos State, Nigeria

Abstract

Energy and power resources must be managed well to guarantee their best use. To ensure that the required demand for energy generation is met, it is necessary to estimate Energy Demand (ED) accurately. Regression analysis and time series analysis were the mainstays of ED prediction in the past. But thanks to recent developments, accuracy has been increased by utilizing machine learning (ML) techniques to identify trends in data on electricity consumption. The purpose of this study is to offer insightful information on the relationships between various variables and how those relationships affect trends in energy usage. The goal is to create a prediction model that can reliably predict ED in a certain study area. In Lagos State, Nigeria, the Elman neural network (ELNN) and support vector machine (SVM) model are used for ED forecasting. The performance of SVM is optimized by selecting suitable kernel functions. The research region's 24-hour dataset was used to train the SVM model. Metrics including the correlation coefficient (R), Pearson correlation coefficient (PCC), mean square error (MSE), mean absolute error (MAE), and Root mean square error (RMSE) were used to evaluate the ML model. The findings show that the ELNN-M3 model fits the data well (R > 0.9). It is noteworthy that the study considered temperature, wind speed, and sun radiation as predicting variables. This study adds to the ongoing attempts to improve the accuracy of energy demand prediction, especially in dynamic contexts such as Lagos State.