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Short Term Electric Load Forecasting using Neural Network and Genetic Algorithm

Mahrufat D. Olagoke, A.A. Ayeni, Moshood A. Hambali. Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Mahrufat D. Olagoke, A.A. Ayeni, Moshood A. Hambali
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  1. Mahrufat D Olagoke, A A Ayeni and Moshood A Hambali. Article: Short Term Electric Load Forecasting using Neural Network and Genetic Algorithm. International Journal of Applied Information Systems 10(4):22-28, January 2016. BibTeX

    	author = "Mahrufat D. Olagoke and A.A. Ayeni and Moshood A. Hambali",
    	title = "Article: Short Term Electric Load Forecasting using Neural Network and Genetic Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	year = 2016,
    	volume = 10,
    	number = 4,
    	pages = "22-28",
    	month = "January",
    	note = "Published by Foundation of Computer Science (FCS), NY, USA"


The major predicament with electricity as a means of transporting energy is that it cannot be stored unlike gas, oil, coal or hydrogen. Due to this, the electric power company faces economical and technical problems in planning, operation and control of electric power system. For the purpose of optimal planning and operation of an electric power system, there is need for appropriate evaluation of the present and future electric load. Electric load forecasting is used by electric power company to anticipate the amount of energy needed to meet up with the demand. Various statistical and artificial intelligence techniques have been applied to short term electric load forecasting in the past but were hampered with some drawbacks. This paper presents another approach for short term load forecasting with lead time of a day ahead (1-24 hours) using artificial neural network (ANN). The hidden layer in ANN model was generated using genetic algorithm instead of the usual practice of trial and error; the ANN model was trained by Levenberg Marquardt. The data (daily load data of 330/132/33KV substation, Ganmo, Kwara State for the month of May, 2014) used in training and validation of the neural network was obtained from the Transmission Company of Nigeria, National Control Centre, Osogbo, Osun State, Nigeria. The model for short term load forecast was designed and implemented with MATLAB package. The result was evaluated by Mean Absolute Percentage Error (MAPE) of 4.705 for the forecasted day.


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Short Term Electric Load Forecasting, Artificial Neural Network, Genetic Algorithm and Levenberg Marquardt