Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption

2014/12/05 by Hasan M. H. Owda, Owda, Hasan M. H., Babatunji Omoniwa +6
Computer Science · Decision Sciences · Engineering · #Artificial Intelligence (cs.AI) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Solar Radiation and Photovoltaics #Stock Market Forecasting Methods #cs.AI #cs.NE

paper · pdf · doi:10.48550/arxiv.1412.2186

10 pages, 5 figures, Journal

openalex publication_date 2014/12/05 · arxiv created 2014/12/06 · arxiv updated 2014/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Due to imprecision and uncertainties in predicting real world problems, artificial neural network (ANN) techniques have become increasingly useful for modeling and optimization. This paper presents an artificial neural network approach for forecasting electric energy consumption. For effective planning and operation of power systems, optimal forecasting tools are needed for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers. Monthly data for electric energy consumed in the Gaza strip was collected from year 1994 to 2013. Data was trained and the proposed model was validated using 2-Fold and K-Fold cross validation techniques. The model has been tested with actual energy consumption data and yields satisfactory performance.

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