The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

被引:5
作者
Laitsos, Vasileios [1 ]
Vontzos, Georgios [1 ]
Paraschoudis, Paschalis [2 ]
Tsampasis, Eleftherios [3 ]
Bargiotas, Dimitrios [1 ]
Tsoukalas, Lefteri H. [4 ]
机构
[1] Univ Thessaly, Dept Elect & Comp Engn, Volos 38334, Greece
[2] Univ Thessaly, Dept Energy Syst, Larisa 41500, Greece
[3] Natl & Kapodestrian Univ Athens, Dept Digital Arts & Cinema, Psahna 34400, Greece
[4] Purdue Univ, Ctr Intelligent Energy Syst CiENS, Sch Nucl Engn, W Lafayette, IN 47906 USA
关键词
electricity load forecasting; electricity price forecasting; wholesale energy market; deep learning; machine learning; statistical models; NEURAL-NETWORKS; OPTIMIZATION; ERROR;
D O I
10.3390/en17225797
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
In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.
引用
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页数:37
相关论文
共 98 条
[1]   Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms [J].
Abumohsen, Mobarak ;
Owda, Amani Yousef ;
Owda, Majdi .
ENERGIES, 2023, 16 (05)
[2]   Comparative Analysis of Electricity Market Prices Based on Different Forecasting Methods [J].
Abunofal, Mustafa ;
Poshiya, Nikhilkumar ;
Qussous, Ramiz ;
Weidlich, Anke .
2021 IEEE MADRID POWERTECH, 2021,
[3]   Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings [J].
Alanazi, Meshari D. ;
Saeed, Ahmad ;
Islam, Muhammad ;
Habib, Shabana ;
Sherazi, Hammad I. ;
Khan, Sheroz ;
Shees, Mohammad Munawar .
SUSTAINABILITY, 2023, 15 (24)
[4]   Robust ensemble learning framework for day-ahead forecasting of household based energy consumption [J].
Alobaidi, Mohammad H. ;
Chebana, Fateh ;
Meguid, Mohamed A. .
APPLIED ENERGY, 2018, 212 :997-1012
[5]  
Amarasinghe K, 2017, PROC IEEE INT SYMP, P1483, DOI 10.1109/ISIE.2017.8001465
[6]   Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning [J].
Andriopoulos, Nikos ;
Magklaras, Aristeidis ;
Birbas, Alexios ;
Papalexopoulos, Alex ;
Valouxis, Christos ;
Daskalaki, Sophia ;
Birbas, Michael ;
Housos, Efthymios ;
Papaioannou, George P. .
APPLIED SCIENCES-BASEL, 2021, 11 (01) :1-22
[7]  
Banitalebi B, 2020, 2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), P28, DOI 10.1109/SSCI47803.2020.9308333
[8]   A Scenario-Based Model Comparison for Short-Term Day-Ahead Electricity Prices in Times of Economic and Political Tension [J].
Baskan, Denis E. ;
Meyer, Daniel ;
Mieck, Sebastian ;
Faubel, Leonhard ;
Kloepper, Benjamin ;
Strem, Nika ;
Wagner, Johannes A. ;
Koltermann, Jan J. .
ALGORITHMS, 2023, 16 (04)
[9]   Multiple households very short-term load forecasting using bayesian networks * [J].
Bessani, Michel ;
Massignan, Julio A. D. ;
Santos, Talysson M. O. ;
London Jr, Joao B. A. ;
Maciel, Carlos D. .
ELECTRIC POWER SYSTEMS RESEARCH, 2020, 189
[10]   An ensemble approach for electricity price forecasting in markets with renewable energy resources [J].
Bhatia, Kushagra ;
Mittal, Rajat ;
Varanasi, Jyothi ;
Tripathi, M. M. .
UTILITIES POLICY, 2021, 70