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
  • [31] Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach
    Belen Bachli, M.
    Sedeno, Lucas
    Ochab, Jeremi K.
    Piguet, Olivier
    Kumfor, Fiona
    Reyes, Pablo
    Torralva, Teresa
    Roca, Maria
    Felipe Cardona, Juan
    Gonzalez Campo, Cecilia
    Herrera, Eduar
    Slachevsky, Andrea
    Matallana, Diana
    Manes, Facundo
    Garcia, Adolfo M.
    Ibanez, Agustin
    Chialvo, Dante R.
    NEUROIMAGE, 2020, 208
  • [32] A machine learning approach to rank the determinants of banking crises over time and across countries
    Casabianca, Elizabeth Jane
    Catalano, Michele
    Forni, Lorenzo
    Giarda, Elena
    Passeri, Simone
    JOURNAL OF INTERNATIONAL MONEY AND FINANCE, 2022, 129
  • [33] Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns
    Sandhan, Tushar
    Srivastava, Tushar
    Sethi, Amit
    Choi, Jin Young
    PROCEEDINGS OF 2013 28TH INTERNATIONAL CONFERENCE ON IMAGE AND VISION COMPUTING NEW ZEALAND (IVCNZ 2013), 2013, : 494 - 499
  • [34] Human skin detection: An unsupervised machine learning way☆
    Islam, A. B. M. Rezbaul
    Alammari, Ali
    Buckles, Bill
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2024, 98
  • [35] A Machine Learning Approach for Collusion Detection in Electricity Markets Based on Nash Equilibrium Theory
    Peyman Razmi
    Majid Oloomi Buygi
    Mohammad Esmalifalak
    JournalofModernPowerSystemsandCleanEnergy, 2021, 9 (01) : 170 - 180
  • [36] A Machine Learning Approach for Collusion Detection in Electricity Markets Based on Nash Equilibrium Theory
    Razmi, Peyman
    Buygi, Majid Oloomi
    Esmalifalak, Mohammad
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2021, 9 (01) : 170 - 180
  • [37] Unsupervised Machine Learning for Card Payment Fraud Detection
    Parreno-Centeno, Mario
    Ali, Mohammed Aamir
    Guan, Yu
    van Moorsel, Aad
    RISKS AND SECURITY OF INTERNET AND SYSTEMS (CRISIS 2019), 2020, 12026 : 247 - 262
  • [38] Unsupervised machine learning framework for early machine failure detection in an industry
    Hasan, Nabeela
    Chaudhary, Kiran
    Alam, Mansaf
    JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2021, 24 (05): : 1497 - 1508
  • [39] Science's disparate responsibilities: Patterns across European countries
    Mejlgaard, Niels
    PUBLIC UNDERSTANDING OF SCIENCE, 2018, 27 (03) : 262 - 275
  • [40] An Unsupervised Machine Learning Approach in Remote Sensing Data
    Mazzei, Mauro
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT III: 19TH INTERNATIONAL CONFERENCE, SAINT PETERSBURG, RUSSIA, JULY 1-4, 2019, PROCEEDINGS, PART III, 2019, 11621 : 435 - 447