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

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.

[1]  Michael Conlon,et al.  A clustering approach to domestic electricity load profile characterisation using smart metering data , 2015 .

[2]  Silvia Santini,et al.  Revealing Household Characteristics from Smart Meter Data , 2014 .

[3]  Ali Al-Wakeel,et al.  Low carbon cities and urban energy systems K-means based cluster analysis of residential smart meter measurements , 2016 .

[4]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[5]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[6]  N.D. Hatziargyriou,et al.  Two-Stage Pattern Recognition of Load Curves for Classification of Electricity Customers , 2007, IEEE Transactions on Power Systems.

[7]  Krzysztof Gajowniczek,et al.  Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms , 2017 .

[8]  Michael E. Webber,et al.  Clustering analysis of residential electricity demand profiles , 2014 .

[9]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[10]  Michela Milano,et al.  User-Aware Electricity Price Optimization for the Competitive Market , 2017 .

[11]  B. De Moor,et al.  Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series , 2005, IEEE Transactions on Power Systems.

[12]  José Luis Díez,et al.  Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers , 2014 .

[13]  Antonio Cezar de Castro Lima,et al.  Typification of load curves for DSM in Brazil for a smart grid environment , 2015 .

[14]  Jianzhong Wu,et al.  k-means based load estimation of domestic smart meter measurements , 2017 .

[15]  Katarzyna Sznajd-Weron,et al.  Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs , 2014 .

[16]  Marco Raberto,et al.  Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics , 2014, AI Commun..

[17]  Thorsten Staake,et al.  Feature extraction and filtering for household classification based on smart electricity meter data , 2014, Computer Science - Research and Development.

[18]  Miin-Shen Yang,et al.  A Generalization of Rand and Jaccard Indices with Its Fuzzy Extension , 2016, Int. J. Fuzzy Syst..

[19]  Marco Raberto,et al.  An agent-based stock-flow consistent model of the sustainable transition in the energy sector , 2018 .

[20]  Krzysztof Gajowniczek,et al.  Electricity forecasting on the individual household level enhanced based on activity patterns , 2017, PloS one.

[21]  P. Rousseeuw,et al.  Displaying a clustering with CLUSPLOT , 1999 .

[22]  P. Postolache,et al.  Customer Characterization Options for Improving the Tariff Offer , 2002, IEEE Power Engineering Review.

[23]  Krzysztof Gajowniczek,et al.  Short term electricity forecasting based on user behavior from individual smart meter data , 2015, J. Intell. Fuzzy Syst..

[24]  Jack Sklansky,et al.  An overview of mapping techniques for exploratory pattern analysis , 1988, Pattern Recognit..

[25]  R. Weron.  Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[26]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.