Abstract
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.
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References
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)
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)
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)
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)
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)
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)
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)
Zhao, Hx, Magoulès, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16(6), 3586–3592 (2012)
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)
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)
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)
Arora, S., Taylor, J.W.: Forecasting electricity smart meter data using conditional kernel density estimation. Omega 59, 47–59 (2016)
Hong, T., Fan, S.: Probabilistic electric load forecasting: a tutorial review. Int. J. Forecast. 32(3), 914–938 (2016)
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Vexler, J., Kramer, S. (2019). Integrating LSTMs with Online Density Estimation for the Probabilistic Forecast of Energy Consumption. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_40
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DOI: https://doi.org/10.1007/978-3-030-33778-0_40
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