Integrating LSTMs with Online Density Estimation for the Probabilistic Forecast of Energy Consumption

  • Julian VexlerEmail author
  • Stefan KramerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)


In machine learning applications in the energy sector, it is often necessary to have both highly accurate predictions and information about the probabilities of certain scenarios to occur. We address this challenge by integrating and combining long short-term memory networks (LSTMs) and online density estimation into a real-time data streaming architecture of an energy trader. The online density estimation is done in the MiDEO framework, which estimates joint densities of data streams based on ensembles of chains of Hoeffding trees. One attractive feature of the solution is that queries can be sent to the here-called forecast-based point density estimators (FPDE) to derive information from a compact representation of two data streams, leading to a new perspective to the problem. The experiments indicate promising application possibilities of FPDE, including but not limited to the estimation of uncertainties, early model evaluation and the simulation of alternative scenarios.


Density estimation Data stream Energy consumption 


  1. 1.
    Zhu, L., Laptev, N.: Deep and confident prediction for time series at uber. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 103–110 (2017)Google Scholar
  2. 2.
    Dai, H., Kozareva, Z., Dai, B., Smola, A., Song, L.: Learning steady-states of iterative algorithms over graphs. In: International Conference on Machine Learning, pp. 1114–1122 (2018)Google Scholar
  3. 3.
    Lin, F., Beadon, M., Dixit, H.D., Vunnam, G., Desai, A., Sankar, S.: Hardware remediation at scale. In: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp. 14–17. IEEE (2018)Google Scholar
  4. 4.
    Geilke, M., Karwath, A., Frank, E., Kramer, S.: Online estimation of discrete, continuous, and conditional joint densities using classifier chains. Data Min. Knowl. Discov. 32(3), 561–603 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Berriel, R., Teixeira Lopes, A., Rodrigues, A., Varejao, F., Oliveira-Santos, T.: Monthly energy consumption forecast: a deep learning approach. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 4283–4290 (2017)Google Scholar
  6. 6.
    Alobaidi, M.H., Chebana, F., Meguid, M.A.: Robust ensemble learning framework for day-ahead forecasting of household based energy consumption. Appl. Energy 212, 997–1012 (2018)CrossRefGoogle Scholar
  7. 7.
    Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841–851 (2017)CrossRefGoogle Scholar
  8. 8.
    Zhao, Hx, Magoulès, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16(6), 3586–3592 (2012)CrossRefGoogle Scholar
  9. 9.
    Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924 (2017)CrossRefGoogle Scholar
  10. 10.
    Kaytez, F., Taplamacioglu, M.C., Cam, E., Hardalac, F.: Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int. J. Electr. Power Energy Syst. 67, 431–438 (2015)CrossRefGoogle Scholar
  11. 11.
    Dedinec, A., Filiposka, S., Dedinec, A., Kocarev, L.: Deep belief network based electricity load forecasting: an analysis of macedonian case. Energy 115, 1688–1700 (2016)CrossRefGoogle Scholar
  12. 12.
    Arora, S., Taylor, J.W.: Forecasting electricity smart meter data using conditional kernel density estimation. Omega 59, 47–59 (2016)CrossRefGoogle Scholar
  13. 13.
    Hong, T., Fan, S.: Probabilistic electric load forecasting: a tutorial review. Int. J. Forecast. 32(3), 914–938 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Johannes Gutenberg-Universität MainzMainzGermany

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