Authors:
Nadeem Iftikhar
;
Akos Madarasz
and
Finn Nordbjerg
Affiliation:
University College of Northern Denmark, Aalborg 9200, Denmark
Keyword(s):
Nearest Neighbors, Unsupervised Learning, KNN, Brute Force, KD Tree, Ball Tree, Similarity Search, Top-K Query.
Abstract:
Gaining insight into household electricity consumption patterns is crucial within the energy sector, particularly for tasks such as forecasting periods of heightened demand. The consumption patterns can furnish insights into advancements in energy efficiency, exemplify energy conservation and demonstrate structural transformations to specific clusters of households. This paper introduces different practical approaches for identifying similar households through their consumption patterns. Initially different data sets are merged, followed by aggregating data to a higher granularity for short-term or long-term forecasts. Subsequently, unsupervised nearest neighbors learning algorithms are employed to find similar patterns. These proposed approaches are valuable for utility companies in offering tailored energy-saving recommendations, predicting demand, engaging consumers based on consumption patterns, visualizing energy use, and more. Furthermore, these approaches can serve to generate
authentic synthetic data sets with minimal initial data. To validate the accuracy of these approaches, a real data set spanning eight years and encompassing 100 homes has been employed.
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