Abstract
Demand Response (DR) can contribute towards the energy efficiency in buildings, which is one of the major concerns among governments, scientists, and researchers. DR programs rely on the anticipation to electric demand peaks, for which the development of short-term electric demand forecasting models may be valuable. This article presents two different variants of the KNN algorithm to predict short-term electric demand for apartments located in Madrid (Spain). On the one hand, the use of an approach based on the estimation of a Machine Learning model (KNFTS) is studied. In this method, time-related and date-related features are used as exploratory variables. On the other hand, a method based on the recognition of similar patterns in the time series (KNPTS) is analysed. The Edit Distance for Real Sequences (EDR), Root Mean Square Error (RMSE) and Dynamic Time Warping (DTW) are used to measure the accuracy of forecasts for both approaches. The experiments demonstrate that the KNPTS has a higher accuracy over the KNFTS when predicting the short-term electric demand. Furthermore, the models’ adaptation to unusual situations is showcased in this article. The impact of the COVID-19 pandemic derived in a worldwide electric demand drop due to the lockdown and other confinement measures, and the retraining method proposed for the KNPTS model has been demonstrated to be valid, as it improves the forecasting accuracy.
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Acknowledgments
This work is partly supported by the RESPOND (integrated demand REsponse Solution towards energy POsitive NeighbourhooDs) and the REACT (Renewable Energy for self-sustAinable island CommuniTies) projects, which have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 768619 and no. 824395 respectively.
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Gomez-Omella, M., Esnaola-Gonzalez, I., Ferreiro, S. (2020). Short-Term Forecasting Methodology for Energy Demand in Residential Buildings and the Impact of the COVID-19 Pandemic on Forecasts. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVII. SGAI 2020. Lecture Notes in Computer Science(), vol 12498. Springer, Cham. https://doi.org/10.1007/978-3-030-63799-6_18
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