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Optimization of Short Load Forecasting in Electricity Market of Iran Using Artificial Neural Networks

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Abstract

Accurate short-term load forecasting (STLF) is one of the essential requirements for power systems. In this paper, two different seasonal artificial neural networks (ANNs) are designed and compared in terms of model complexity, robustness, and forecasting accuracy. Furthermore, the performance of ANN partitioning is evaluated. The first model is a daily forecasting model which is used for forecasting hourly load of the next day. The second model is composed of 24 sub-networks which are used for forecasting hourly load of the next day. In fact, the second model is partitioning of the first model. Time, temperature, and historical loads are taken as inputs for ANN models. The neural network models are based on feed-forward back propagation which are trained and tested using data from electricity market of Iran during 2003 to 2005. Results show a good correlation between actual data and ANN outcomes. Moreover, it is shown that the first designed model consisting of single ANN is more appropriate than the second model consisting of 24 distinct ANNs. Finally ANN results are compared to conventional regression models. It is observed that in most cases ANN models are superior to regression models in terms of mean absolute percentage error (MAPE).

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Acknowledgements

The authors are grateful for the valuable comments and suggestion from the respected reviewers. Their valuable comments and suggestions have enhanced the strength and significance of our paper. This study was supported by a grant from University of Tehran (Grant No. 25652/1/01). The authors are grateful for the support provided by the College of Engineering, University of Tehran, Iran.

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Authors and Affiliations

  1. Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanic, College of Engineering, University of Tehran, Tehran, Iran

    Ali Azadeh, Seyyed Farid Ghaderi, Mohammad Sheikhalishahi & Behnaz Pourvalikhan Nokhandan

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  1. Ali Azadeh
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  2. Seyyed Farid Ghaderi
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  3. Mohammad Sheikhalishahi
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  4. Behnaz Pourvalikhan Nokhandan
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Correspondence to Ali Azadeh.

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Azadeh, A., Ghaderi, S.F., Sheikhalishahi, M. et al. Optimization of Short Load Forecasting in Electricity Market of Iran Using Artificial Neural Networks. Optim Eng 15, 485–508 (2014). https://doi.org/10.1007/s11081-012-9200-8

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  • DOI: https://doi.org/10.1007/s11081-012-9200-8

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