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Segmenting Residential Smart Meter Data for Short-Term Load Forecasting
2018
Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy '18
In order to reliably generate electricity to meet the demands of the customer base, it is essential to match supply with demand. Short-term load forecasting is utilised in both real-time scheduling of electricity, and load-frequency control. This paper aims to improve the accuracy of load-forecasting by using machine learning techniques to predict 30 minutes ahead using smart meter data. We utilised the k-means clustering algorithm to cluster similar individual consumers and fit distinct models
doi:10.1145/3208903.3208923
dblp:conf/eenergy/KellMF18
fatcat:y4rpwvp52nbspnmxw4shnd4hqi