Big Data Analytics for Price and Load Forecasting in Smart Grids

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 25)


This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load that is difficult to process with conventional computational models, referred as big data. The processing and analyzing of big data divulges the deeper insights that help experts in improvement of smart grid operations. Processing and extracting of the meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand on big data using deeper Long Short-Term Memory (LSTM). Due to adaptive and automatic feature learning of DNNs, processing of big data is easier with LSTM as compared to purely data driven methods. The proposed model is evaluated using a well-known real electricity markets’ data.


Smart grid Big data Electricity load Price forecasting Long Short-Term Memory LSTM 


  1. 1.
    Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)CrossRefGoogle Scholar
  2. 2.
    Jiang, H., Wang, K., Wang, Y., Gao, M., Zhang, Y.: Energy big data: a survey. IEEE Access 4, 3844–3861 (2016)CrossRefGoogle Scholar
  3. 3.
    Liu, J.P., Li, C.L.: The short-term power load forecasting based on sperm whale algorithm and wavelet least square support vector machine with DWT-IR for feature selection. Sustainability 9(7), 1188–1208 (2017)CrossRefGoogle Scholar
  4. 4.
    Ghasemi, A., Shayeghi, H., Moradzadeh, M., Nooshyar, M.: A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management. Appl. Energy 177, 40–59 (2016)CrossRefGoogle Scholar
  5. 5.
    Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.: Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data (2017, accepted)Google Scholar
  6. 6.
    Fan, C., Xiao, F., Zhao, Y.: A short-term building cooling load prediction method using deep learning algorithms. Appl. Energy 195, 222–233 (2017)CrossRefGoogle Scholar
  7. 7.
    Ryu, S., Noh, J., Kim, H.: Deep neural network based demand side short term load forecasting. Energies 10(1), 3–23 (2016)CrossRefGoogle Scholar
  8. 8.
    Bouktif, S., Fiaz, A., Ouni, A., Serhani, M.A.: Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11(7), 1–20 (2018)CrossRefGoogle Scholar
  9. 9.
    Ugurlu, U., Oksuz, I., Tas, O.: Electricity price forecasting using recurrent neural networks. Energies 11(5), 1–23 (2018)CrossRefGoogle Scholar
  10. 10.
    Kuo, P.H., Huang, C.J.: An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4), 1280 (2018)CrossRefGoogle Scholar
  11. 11.
    Moghaddass, R., Wang, J.: A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Trans. Smart Grid (2017, accepted)Google Scholar
  12. 12.
    Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Inf. Fusion 42, 146–157 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.Abasyn University Department of Computing and TechnologyIslamabadPakistan

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