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Energies 2018, 11(3), 514;

Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques

Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland
Author to whom correspondence should be addressed.
Received: 22 January 2018 / Revised: 19 February 2018 / Accepted: 23 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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Individual electricity customers that are connected to low voltage network in Poland are usually assigned to the most common G11 tariff group with flat prices for the whole year, no matter the usage volume. Given the diversity of customers’ behavior inside the same specific group, we aim to propose an approach to assign the customers based on some objective factors rather than subjective fixed assignment. With the smart metering data and statistical methods for clustering we can explore and recommend each customer the most suitable tariff to benefit from lower prices thus generate the savings. Further, the paper applies hierarchical, k-means and Kohonen approaches to assign the customers to the proper tariff, assuming that the customer can gain the biggest expenses reduction from the tariff switch. The analysis was conducted based on the Polish dataset with an hourly energy readings among 197 entities. View Full-Text
Keywords: unsupervised machine learning; electricity forecasting; end users characteristics unsupervised machine learning; electricity forecasting; end users characteristics

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Nafkha, R.; Gajowniczek, K.; Ząbkowski, T. Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques. Energies 2018, 11, 514.

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