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

• Main Authors: Rafik Nafkha, Krzysztof Gajowniczek, Tomasz Ząbkowski
• Format: Article
• Language: English
• Published: MDPI AG 2018-02-01
• Series: Energies
• Subjects: unsupervised machine learning; electricity forecasting; end users characteristics
• Online Access: http://www.mdpi.com/1996-1073/11/3/514

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.