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A Comparative Analysis of Neural Networks and Enhancement of ELM for Short Term Load Forecasting

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Complex, Intelligent, and Software Intensive Systems (CISIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 993))

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Abstract

Smart grid is the evolved form of traditional power grid with the integration of sensing, communication, computing, monitoring and controlling technologies. These technologies make the power grid reliable, efficient and economical. Smart grid enable users to make bidding on the basis of demand side management models. Demand side management can be made responsive and efficient by effective and accurate load forecasting. Accurate load forecasting is an important but challenging task because of irregular and non-linear consumption of individual users and industrial consumers. Different approaches have been proposed for load forecasting, but artificial intelligent models, specifically ANNs perform well for short, medium and long term load forecasting. The main focus of this paper is to present a comparative analysis of NNs for short term load forecasting. NYISO dataset is used for experiments. XGboost and decision tree are used for features importance calculation and RFE is used for features extraction on the basis of score assigned. Three basic techniques (CNN, MLP and ELM) are used for forecasting. Furthermore, ELM is enhanced and E_ELM is proposed for STLF. Moreover, results are evaluated on four statistical measures (MAPE, MAE, MSE and RMS).

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Author information

Authors and Affiliations

  1. COMSATS University Islamabad, Islamabad, 44000, Pakistan

    Rahim Ullah, Nadeem Javaid, Ghulam Hafeez, Salim Ullah & Ashraf Ullah

  2. Higher Education Department, KP, Peshawar, 25000, Pakistan

    Rahim Ullah

  3. Wah Engineering College, University of Wah, Wah Cantt, 47040, Pakistan

    Ghulam Hafeez

  4. International Islamic University, Islamabad, 44000, Pakistan

    Fahad Ahmad

Authors
  1. Rahim Ullah
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  2. Nadeem Javaid
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  3. Ghulam Hafeez
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  4. Salim Ullah
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  5. Fahad Ahmad
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  6. Ashraf Ullah
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Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Faculty of Information Engineering, Fukuoka, Japan

    Leonard Barolli

  2. School of Software, University of Technology Sydney (UTS), Ultimo, NSW, Australia

    Farookh Khadeer Hussain

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Faculty of Information Engineering, Fukuoka, Japan

    Makoto Ikeda

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Ullah, R., Javaid, N., Hafeez, G., Ullah, S., Ahmad, F., Ullah, A. (2020). A Comparative Analysis of Neural Networks and Enhancement of ELM for Short Term Load Forecasting. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_7

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