Abstract
The rollout of smart meters and steadily increasing sample rates lead to a growing amount of raw data available for short-term load forecasting (STLF). While the original motivation for high resolutions has been the enabling of non-intrusive load monitoring (NILM), so far their value for STLF has been limited. We propose a novel approach, which allows the exploitation of high resolution data for STLF, by incorporating NILM and subsequent clustering of similarly behaving appliances as a preprocessing step.
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Wurm, M., Coroamă, V.C. Poster abstract: grid-level short-term load forecasting based on disaggregated smart meter data. Comput Sci Res Dev 33, 265–266 (2018). https://doi.org/10.1007/s00450-017-0374-3
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DOI: https://doi.org/10.1007/s00450-017-0374-3