Abstract
Current demands on the energy market, such as legal policies towards green energy usage and economic pressure due to growing competition, require energy companies to increase their understanding of consumer behavior and streamline business processes. One way to help achieve these goals is by making use of the increasing availability of smart metering time series. In this paper we extend an approach based on fuzzy clustering using smart meter data to yield load profiles which can be used to forecast the energy demand of customers. In addition, our approach is built with existing business processes in mind. This helps not only to accurately satisfy real world requirements, but also to ease adoption by the industry. We also assess the quality of our approach using real world smart metering datasets.
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Bock, C. (2018). Forecasting Energy Demand by Clustering Smart Metering Time Series. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_37
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DOI: https://doi.org/10.1007/978-3-319-91473-2_37
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