Abstract
Short Term Load Forecasting is an essential component for optimizing the energy management of individual houses or small micro grids. By learning consumption patterns on smart metering data, smart grid applications such as Demand-Side-Management can be applied. However, most of the research done in this field is based on data which is not publicly available. Moreover, the evaluations also vary in the evaluation settings and the error measurements. In this work, five state-of-the-art approaches are compared on three publicly available datasets in the most common scenarios. By doing this, the most promising methods and model settings are pointed out. Furthermore, it can be seen that forecasting the consumption 24 h ahead achieves about the same accuracy as doing it four hours ahead. Still, the best results for individual households are rather inaccurate. By aggregating ten households, the results enhance by a factor of about 60%.
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Notes
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For example, in Germany the installation of smart meters in new buildings has been enforced since 2010 by law, cf. 21b Abs. 3a EnWG.
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Gerwig, C. (2017). Short Term Load Forecasting for Residential Buildings. In: Hu, J., Leung, V., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds) Smart Grid Inspired Future Technologies. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-319-47729-9_8
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DOI: https://doi.org/10.1007/978-3-319-47729-9_8
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