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    Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

    Vin Cent Tai1,*, Yong Chai Tan1, Nor Faiza Abd Rahman1, Hui Xin Che2, Chee Ming Chia2, Lip Huat Saw3, Mohd Fozi Ali4

    Energy Engineering, Vol.118, No.3, pp. 715-725, 2021, DOI:10.32604/EE.2021.014865

    Abstract Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) structure has been designed and tested for this purpose. Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030. Pearson correlation was used to examine the input variables for model construction. The analysis indicates that Primary Energy Supply (PES), population, Gross Domestic Product (GDP) and temperature are strongly correlated. The forecast results by the proposed… More >

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