Abstract
In traditional power networks, Distribution System Operators (DSOs) used to monitor energy flows on a medium- or high-voltage level for an ensemble of consumers and the low-voltage grid was regarded as a black box. However, electric utilities nowadays obtain ever more precise information from single consumers connected to the low- and medium-voltage grid thanks to smart meters (SMs). This allows a previously unattainable degree of detail in state estimation and other grid analysis functionalities such as predictions. This paper focuses on the use of Artificial Neural Networks (ANNs) for accurate short-term load and Photovoltaic (PV) predictions of SM profiles and investigates different spatial aggregation levels. A concluding power flow analysis confirms the benefits of time series prediction to support grid operation. This study is based on the SM data available from more than 40,000 consumers as well as PV systems in the City of Basel, Switzerland.
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Acknowledgements
This study is part of the project “Optimized Distribution Grid Operation by Utilization of SmartMetering Data” funded by the Swiss Federal Office of Energy and carried out at the ETH Zurich in collaboration with the ETH spin-off company Adaptricity [12] and the public utility of the City of Basel “Industrielle Werke Basel” (IWB).
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Zufferey, T., Ulbig, A., Koch, S., Hug, G. (2017). Forecasting of Smart Meter Time Series Based on Neural Networks. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2016. Lecture Notes in Computer Science(), vol 10097. Springer, Cham. https://doi.org/10.1007/978-3-319-50947-1_2
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DOI: https://doi.org/10.1007/978-3-319-50947-1_2
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