Customer Characterization Options for Improving the Tariff Offer

By Gianfranco Chicco, Roberto Napoli, P. Postolache, M. Scutariu, C. Toader. IEEE Power Engineering Review, 2002

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Comparisons among clustering techniques for electricity customer classification (2006, methodology): “The clustering validity indicators considered in this paper have been defined according to a metric that merges the information on the compactness of the load patterns belonging to the same class and (inversely) on the inter-distance among the CRLPs [1], [9], [12].”
Big Data Analytics for Dynamic Energy Management in Smart Grids (2015, methodology): “The data mining process involves the utilization of algorithms for discovering patterns among the data following a similarity criterion [42].”
Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method (2008, methodology): “It has been used for many years for tariff formulation, system planning, and devising marketing strategies [ 12 ].”
Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China (2018, methodology): “For the mixedF, a higher value of the index indicates better performance, whereas for the DB and MIA indexes, a smaller value of the index indicates better performance.”