K nearest neighbour optimal selection in fuzzy inductive reasoning for smart grid applications
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
Energy recasting has been an area of great interest in the last years. It unlocks, not only the Smart Grid's potential with load balancing but also new business models and added value services. To achieve an accurate, robust and fast prediction, model's parametrization is key and becomes a bottleneck in the value-chain. In this article, we present an improved version of Flexible Fuzzy Inductive Reasoning (Flexible FIR) that selects the most optimal number of nearest neighbours during FIR prediction phase, called K nearest neighbour Optimal Selection (KOS). To this end, a real smart grid forecasting application, i.e. electricity load forecasting, has been chosen in this study. The results show that the best forecasting accuracy, on average, is when the KOS is used on Flexible FIR. While with KOS the optimal parameter k is found online, without it is not, which increases the computational time.
CitationJurado, S.; Nebot, A.; Múgica, F. K nearest neighbour optimal selection in fuzzy inductive reasoning for smart grid applications. A: IEEE International Conference on Fuzzy Systems. "2019 IEEE International Conference on Fuzzy Systems: New Orleans, Louisiana, USA, June 23-26, 2019". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1-6.
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