TAILIEUCHUNG - Electrical energy demand forecasting model using artificial neural network: A case study of La-gos State Nigeria

This paper presents an Artificial Neural Network based method for Electrical Energy Demand Forecasting using a case study of Lagos state, Nigeria. The predicted values are compared with actual values to estimate the performance of the proposed technique. | Electrical energy demand forecasting model using artificial neural network A case study of La-gos State Nigeria International Journal of Data and Network Science 3 2019 305 322 Contents lists available at GrowingScience International Journal of Data and Network Science homepage ijds Electrical energy demand forecasting model using artificial neural network A case study of La- gos State Nigeria Khadeejah Adebisi Abdulsalama and Olubayo Moses Babatundea a Department of Electrical amp Electronics Engineering University Of Lagos Akoka Nigeria CHRONICLE ABSTRACT Article history Electrical Energy is an essential commodity which significantly contributes to the economic de- Received January 01 2019 velopment of any country. Many non-linear factors contribute to the final output of electrical en- Received in revised format March ergy demand. In order to efficiently predict electrical energy demand many time-series analysis 6 2019 and multivariate techniques have been suggested. In order for these methods to accurately work Accepted May 24 2019 Available online May 24 2019 an enormous quantity of historical dataset is essential which sometimes are not available inade- Keywords quate and inaccurate. To overcome some of these challenges this paper presents an Artificial Artificial Neural Network Neural Network based method for Electrical Energy Demand Forecasting using a case study of Electrical Energy Demand Fore- Lagos state Nigeria. The predicted values are compared with actual values to estimate the perfor- casting mance of the proposed technique. Recurrent Neural Network 2019 by the authors licensee Growing Science Canada. 1. Introduction Modern science and engineering studies use models to describe physical biological and social systems and experimental data are used to verify and estimate such models. However in many real life systems the underlying systems are either unknown or sometimes the systems are too complex for concise math- ematical .

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