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A Comparative Study of Regression Analysis and Artificial Neural Network Methods for Medium-Term Load Forecasting

Samuel, I. A. and Adetiba, E. and Odigwe, I. A. and Felly-Njoku, Firstlady C. (2017) A Comparative Study of Regression Analysis and Artificial Neural Network Methods for Medium-Term Load Forecasting. Indian Journal of Science and Technology, 10 (10). ISSN 0974-5645

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Abstract

Load forecasting is an operation of predicting the future of load demands in electrical systems using previous or historical data. This paper reports the study on a medium-term load forecast carried out with load demand data set obtained from Covenant University campus in Nigeria and carry out comparative study of the two methods used in this paper. Methods/Statistical analysis: The regression analysis and Artificial Neural Network (ANN) models were used to show the feasibility of generating an accurate medium-term load forecast for the case study despite the peculiarity of its load data. The statistical evaluation methods used are Mean Absolute Percentage Error (MAPE) and root mean square error. Findings: The results from the comparative study show that the ANN model is a superior method for load forecast due to its ability to handle the load data and it has lower MAPE and RMSE of 0.0285 and 1.124 respectively which is far better result than the regression model. Application/Improvements: This result provides a benchmark for power system planning and future studies in this research domain.

Item Type: Article
Uncontrolled Keywords: Artificial Neural Networks, Load Forecasting, Medium-Term, MAPE, Regression Analysis, RMSE
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 19 Jun 2017 07:12
Last Modified: 19 Jun 2017 07:12
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/8256

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