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An Innovative Model Based on FCRBM for Load Forecasting in the Smart Grid

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Book cover Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 994))

Abstract

In this paper, an efficient model based on factored conditional restricted boltzmann machine (FCRBM) is proposed for electric load forecasting of in smart grid (SG). This FCRBM has deep layers structure and uses rectified linear unit (RELU) function and multivariate autoregressive algorithm for training. The proposed model predicts day ahead and week ahead electric load for decision making of the SG. The proposed model is a hybrid model having four modules i.e., data processing and features selection module, FCRBM based forecaster module, GWDO (genetic wind driven optimization) algorithm-based optimizer module, and utilization module. The proposed model is examined using FE grid data of USA. The proposed model provides more accurate results with affordable execution time than other load forecasting models, i.e., mutual information, modified enhanced differential evolution algorithm, and artificial neural network (ANN) based model (MI-mEDE-ANN), accurate fast converging short term load forecasting model (AFC-STLF), Bi-level model, and features selection and ANN-based model (FS-ANN).

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Author information

Authors and Affiliations

  1. COMSATS University Islamabad, Islamabad, 44000, Pakistan

    Ghulam Hafeez, Nadeem Javaid & Ammar Ali

  2. University of Engineering and Technology, Mardan, 23200, Pakistan

    Ghulam Hafeez & Muhammad Riaz

  3. Bahria University Islambad, Islamabd, 44000, Pakistan

    Khalid Umar

  4. PMAS Agriculture University, Rawalpindi, 46000, Pakistan

    Zafar Iqbal

Authors
  1. Ghulam Hafeez
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  2. Nadeem Javaid
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  3. Muhammad Riaz
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  4. Khalid Umar
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  5. Zafar Iqbal
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  6. Ammar Ali
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Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

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

    Leonard Barolli

  2. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  3. School of Business, University of New South Wales, Canberra, ACT, Australia

    Omar K. Hussain

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Hafeez, G., Javaid, N., Riaz, M., Umar, K., Iqbal, Z., Ali, A. (2020). An Innovative Model Based on FCRBM for Load Forecasting in the Smart Grid. In: Barolli, L., Xhafa, F., Hussain, O. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2019. Advances in Intelligent Systems and Computing, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-22263-5_5

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