Forecasting electrical consumption by integration of Neural Network, time series and ANOVA

Due to various seasonal and monthly changes in electricity consumption, it is difficult to model it with conventional methods. This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network for the electrical consumption forecasting. In or...

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Bibliographic Details
Published in:Applied mathematics and computation 2007-03, Vol.186 (2), p.1753-1761
Main Authors: Azadeh, A., Ghaderi, S.F., Sohrabkhani, S.
Format: Article
Language:English
Subjects:
ANOVA
Artificial Neural Network
Electricity consumption
Exact sciences and technology
Forecasting
Mathematical analysis
Mathematics
Measure and integration
Numerical analysis
Numerical analysis. Scientific computation
Preprocessing
Sciences and techniques of general use
Time series
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Summary:Due to various seasonal and monthly changes in electricity consumption, it is difficult to model it with conventional methods. This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network for the electrical consumption forecasting. In order to train the ANN, preprocessed data have been extracted from the time series techniques. This is the first study which uses ANN and time series for forecasting electrical consumption. Previous studies based their verification by the difference error estimation. However, this study shows the advantage of ANN methodology through analysis of variance (ANOVA). Furthermore, actual data are compared with ANN and conventional regression model. To show the applicability and superiority of the ANN and time series approach, monthly electricity consumption in Iran for the past 20 years was collected to train and test the network.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2006.08.094