Abstract
A power grid operation on all levels, including regional, becomes more and more dependent on ability to analyze and predict future electrical consumption. Measuring devices in the system at the same time generates increasing amount of data. Defining the consumption patterns helps to better serve the users and system operator. Such an analysis is only possible with the use of modern data mining techniques. This paper examines correlation between region consumption itself, and available meteorological data based on three years dataset with half an hour resolution for regional energy system. Moreover, the authors propose to use the speed of change in the consumption of the region as one of the newly analyzed factors to update the dataset. The methods for data preprocessing, data cleaning, statistical analysis, such as correlation analysis with the available dataset structure are discussed. Unexpected correlation between consumption and wind speed data in the region is presented. As a practical example of Data Mining Techniques use, cluster analysis of the consumption and the speed of change in the consumption of the region is presented. The results show the ability to use this method to define outlying values in speed of consumption change, which could be the result of frequency change and support from supply energy system.
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Nikishin, A., Parshilkina, A., Tristanov, A. (2023). Regional Power System Consumption Analysis with Data Mining Techniques. In: Kostrikova, N. (eds) Energy Ecosystems: Prospects and Challenges. EcoSystConfKlgtu 2022. Lecture Notes in Networks and Systems, vol 626. Springer, Cham. https://doi.org/10.1007/978-3-031-24820-7_4
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DOI: https://doi.org/10.1007/978-3-031-24820-7_4
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