Abstract
Accurate power demand forecasts can help power distributors to lower differences between contracted and demanded electricity and minimize the imbalance in grid and related costs. Our forecasting method is designed to process continuous stream of data from smart meters incrementally and to adapt the prediction model to concept drifts in power demand. It identifies drifts using a condition based on an acceptable distributor’s daily imbalance. Using only the most recent data to adapt the model (in contrast to all historical data) and adapting the model only when the need for it is detected (in contrast to creating a whole new model every day) enables the method to handle stream data. The proposed model shows promising results.
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Bosnić, Z. et al.: Enhancing data stream predictions with reliability estimators and explanation. Eng. Appl. Artif. Intell. 34(C), 178–192 (2014)
Bouchachia, A.: Fuzzy classification in dynamic environments. Soft. Comput. 15(5), 1009–1022 (2011)
Box, G.E.P., et al.: Time Series Analysis: Forecasting and Control, 4th edn. Wiley, New Jersey (2008)
Dannecker, L., Schulze, R., Böhm, M., Lehner, W., Hackenbroich, G.: Context-aware parameter estimation for forecast models in the energy domain. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 491–508. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22351-8_33
Gama, J., et al.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 1–37 (2014)
Gama, J.: Data stream mining: the bounded rationality. Informatica 37(1), 21–25 (2013)
Hong, T.: Energy forecasting: past, present and future. Foresight Int. J. Appl. Forecast. 32, 43–48 (2014)
Hong, W.-C.: Intelligent Energy Demand Forecasting. Springer, London (2013)
Ma, S., et al.: The variable weight combination load forecasting based on grey model and semi-parametric regression model. In: 2013 IEEE International Conference IEEE Region 10 (TENCON 2013), pp. 1–4. IEEE, Xi’an (2013)
Taylor, J.W.: Short-term electricity demand forecasting using double seasonal exponential smoothing. J. Oper. Res. Soc. 54(8), 799–805 (2003)
Taylor, J.W.: Smooth transition exponential smoothing. J. Forecast. 23(6), 385–404 (2004)
Taylor, J.W., McSharry, P.E.: Short-term load forecasting methods: an evaluation based on european data. IEEE Trans. Power Syst. 22(4), 2213–2219 (2007)
Winters, P.R.: Forecasting sales by exponentially weighted moving averages. Manag. Sci. 6(3), 324–342 (1960)
Žliobaite, I.: Learning under Concept Drift: an Overview. Vilnius University (2010)
Acknowledgements
This contribution was created with the support of the Research and Development Operational Programme for the project “International Centre of Excellence for Research of Intelligent and Secure Information-Communication Technologies and Systems”, ITMS 26240120039, co-funded by the ERDF; the Scientific Grant Agency of the Slovak Republic, grants No. VG 1/0752/14 and VG 1/0646/15 and the STU Grant scheme for Support of Young Researchers.
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Vrablecová, P., Rozinajová, V., Bou Ezzeddine, A. (2017). Incremental Adaptive Time Series Prediction for Power Demand Forecasting. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_9
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DOI: https://doi.org/10.1007/978-3-319-61845-6_9
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