Short Term Load Forecasting based on Deep Learning for Smart Grid Applications

  • Ghulam Hafeez
  • Nadeem JavaidEmail author
  • Safeer Ullah
  • Zafar Iqbal
  • Mahnoor Khan
  • Aziz Ur Rehman
  • Ziaullah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)


Short term load forecasting is indispensable for industrial, commercial, and residential smart grid (SG) applications. In this regard, a large variety of short term load forecasting models have been proposed in literature spaning from legacy time series models to contemporary data analytic models. Some of these models have either better performance in terms of accuracy while others perform well in convergence rate. In this paper, a fast and accurate short term load forecasting framework based on stacked factored conditional restricted boltzmann machine (FCRBM) and conditional restricted boltzmann machine (CRBM) is presented. The stacked FCRBM and CRBM are trained using rectified linear unit (RelU) and sigmoid functions, respectively. The proposed framework is applied to offline demand side load data of US utility. Load forecasts decide weather to increase or decrease the generation of an already running generator or to add extra units or exchange power with neighboring systems. Three performance metrics i.e., mean absolute percentage error (MAPE), normalized root mean square (NRMSE), and correlation coefficient are used to validate the proposed framework. The results show that stacked FCRBM and CRBM are accurate and robust as compared to artificial neural network (ANN) and convolutional neural network (CNN).


Short-term Load Forecasting Conditional Restricted Boltzmann Machine (CRBM) Mean Absolute Percentage Error (MAPE) Average Root Mean Square (NRMSE) Deep Learning Techniques 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Ghulam Hafeez
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Safeer Ullah
    • 1
  • Zafar Iqbal
    • 2
  • Mahnoor Khan
    • 1
  • Aziz Ur Rehman
    • 1
    • 3
  • Ziaullah
    • 4
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.PMAS Arid Agriculture UniversityRawalpindiPakistan
  3. 3.The University of LahoreGujratPakistan
  4. 4.Huazhong University of Science and TechnologyWuhanChina

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