Short-Term Load Forecasting Using The Time Series And Artificial Neural Network Methods
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Forecasting of electrical load is very crucial to the effective and efficient operation of any power
system. This is achieved by obtaining the most accurate forecast which help in minimizing the risk in decision
making and reducesthe costs of operating the power plant. Therefore, the comparative study of time series
and artificial neural network methods for short term load forecasting is carried out in this paper using real
time load data of Covenant University,withthe moving average, exponential smoothing (time series method) and
the Artificial Neural Network (ANN) models. The work was done for the day-to-day operation of the soon-to-becompleted
power station of the university. For each of the methods, models were developed for the load
forecast. The Artificial Neural Network proved to be the best forecast method when the results are compared in
terms of error measurementswith amean absolutedeviation(MAD) having 0.225, mean squared error (MSE)
having 0.095 and the mean absolute percent error(MAPE)having 8.25.
Keywords
TK Electrical engineering. Electronics Nuclear engineering