NDLI: Artificial Neural Networks and regression approaches comparison for forecasting Iran's annual electricity load

Content Provider	IEEE Xplore Digital Library
Author	Ghanbari, A. Naghavi, A. Ghaderi, S.F. Sabaghian, M.
Copyright Year	2009
Description	Author affiliation: Department of Industrial Engineering, College of Engineering, University of Tehran, Iran, P.O. Box 11155-4563 (Ghanbari, A.; Naghavi, A.; Ghaderi, S.F.; Sabaghian, M.)
Abstract	Electrical load forecasting is one of the important concerns of power systems and has been studied from different views. Electrical load forecast might be performed over different time intervals of short, medium and long term. Various techniques have been proposed for short term, medium term or long term load forecasting. In this study we employ Artificial Neural Networks (ANN) and regression (Linear and Log-Linear) approaches for annual electricity load forecasting. This study presents a model that is affected by two economical parameters which are Real-GDP and Population. Using Real-GDP instead of nominal-GDP can provide more accuracy because the effects of inflation are considered in the structure of such model and this will cause the results to be more reliable. To improve forecasting accuracy of the model we apply data preprocessing techniques. Forecasting capability of each approach is evaluated by calculating three separate statistical evaluations of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). All evaluations indicate that the accuracy of ANN which is trained with preprocessed data is remarkably better than the other two conventional approaches.
Starting Page	675
Ending Page	679
File Size	157415
Page Count	5
File Format	PDF
ISBN	9781424446117
DOI	10.1109/POWERENG.2009.4915245
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-18
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy consumption Log-Linear Regression Data Preprocessing Artificial neural networks Power generation economics Power system economics Power system planning Predictive models Electrical Load Forecasting Load forecasting Artificial Neural Networks (ANN) Linear Regression Fuel economy Economic forecasting Power systems
Content Type	Text
Resource Type	Article

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