On the detection of patterns in electricity prices across European countries: An unsupervised machine learning approach

被引:5
|
作者
Saligkaras, Dimitrios [1 ]
Papageorgiou, Vasileios E. [1 ]
机构
[1] Aristotle Univ Thessaloniki, Dept Math, Thessaloniki 54124, Greece
关键词
clustering algorithms; electricity prices; Partition Around Medoids; hierarchical clustering; household incomes; unsupervised machine learning;
D O I
10.3934/energy.2022054
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The year 2022 is characterized by a generalized energy crisis, which leads to steadily increasing electricity prices around the world, while the corresponding salaries remain stable. Therefore, examining trends in electricity prices relative to existing income levels can provide valuable insights into the overpricing/underpricing of energy consumption. In this article, we examine the tendencies of 35 European countries according to their national kWh prices and the average household incomes. We use a series of established clustering methods that leverage available information to reveal price and income patterns across Europe. We obtain important information on the balance between family earnings and electricity prices in each European country and are able to identify countries and regions that offer the most and least favorable economic conditions based on these two characteristics studied. Our analysis reveals the existence of four price and income patterns that reflect geographical differences across Europe. Countries such as Iceland, Norway, and Luxembourg exhibit the most favorable balance between prices and earnings. Conversely, electricity prices appear to be overpriced in many southern and eastern countries, with Portugal being the most prominent example of this phenomenon. In general, average household incomes become more satisfactory for European citizens as we move from east to west and south to north. In contrast, the respective national electricity prices do not follow this geographical pattern, leading to notable imbalances. After identifying significant cases of inflated prices, we investigate the respective causes of the observed situation with the aim of explaining this extreme behavior with exogenous factors. Finally, it becomes clear that the recent increase in energy prices should not be considered as a completely unexpected event, but rather as a phenomenon that has occurred and developed gradually over the years.
引用
收藏
页码:1146 / 1164
页数:19
相关论文
共 50 条
  • [1] Forecasting Electricity Prices: A Machine Learning Approach
    Castelli, Mauro
    Groznik, Ales
    Popovic, Ales
    ALGORITHMS, 2020, 13 (05)
  • [2] Patterns and correlations in European electricity prices
    Trebbien, Julius
    Tausendfreund, Anton
    Gorjao, Leonardo Rydin
    Witthaut, Dirk
    CHAOS, 2024, 34 (07)
  • [3] Lack of Global Convergence and the Formation of Multiple Welfare Clubs across Countries: An Unsupervised Machine Learning Approach
    Mendez, Carlos
    ECONOMIES, 2019, 7 (03)
  • [4] A combined unsupervised learning approach for electricity theft detection and loss estimation
    Xu, Liangcai
    Shao, Zhenguo
    Chen, Feixiong
    IET ENERGY SYSTEMS INTEGRATION, 2023, 5 (02) : 213 - 227
  • [5] Student and school performance across countries: A machine learning approach
    Masci, Chiara
    Johnes, Geraint
    Agasisti, Tommaso
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2018, 269 (03) : 1072 - 1085
  • [6] Predicting and explaining corruption across countries: A machine learning approach
    Lima, Marcio Salles Melo
    Delen, Dursun
    GOVERNMENT INFORMATION QUARTERLY, 2020, 37 (01)
  • [7] The role of renewable energy sources in residential electricity prices: A club convergence analysis across selected European countries
    Bhattacharya, Mita
    Inekwe, John N.
    Liddle, Brantley
    APPLIED ECONOMICS, 2023, 55 (44) : 5157 - 5171
  • [8] Forecasting electricity prices with machine learning: predictor sensitivity
    Naumzik, Christof
    Feuerriegel, Stefan
    INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT, 2021, 15 (01) : 58 - 80
  • [9] Forecasting electricity consumption of OECD countries: A global machine learning modeling approach
    Sen, Doruk
    Tunc, K. M. Murat
    Gunay, M. Erdem
    UTILITIES POLICY, 2021, 70
  • [10] Detection of overdose and underdose prescriptions-An unsupervised machine learning approach
    Nagata, Kenichiro
    Tsuji, Toshikazu
    Suetsugu, Kimitaka
    Muraoka, Kayoko
    Watanabe, Hiroyuki
    Kanaya, Akiko
    Egashira, Nobuaki
    Ieiri, Ichiro
    PLOS ONE, 2021, 16 (11):