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


  • Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria
  • Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Delta State, Nigeria


Objectives: 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.


Artificial Neural Networks, Load Forecasting, Medium-Term, MAPE, Regression Analysis, RMSE.

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