Methodology for electricity price forecasting in the long run

被引:3
作者
Sisodia, Gyanendra Singh [1 ,2 ]
机构
[1] Amrita Vishwavidyapeetham, Amrita Sch Business, Coimbatore, Tamil Nadu, India
[2] Univ Ghent, Fac Econ, Dept Mkt, Ghent, Belgium
来源
INTERNATIONAL SCIENTIFIC CONFERENCE - ENVIRONMENTAL AND CLIMATE TECHNOLOGIES, CONECT 2015 | 2016年 / 95卷
关键词
electricity price forecasting; long run electricity prices; energy mix; carbon price; CONSUMPTION-GROWTH NEXUS; PANEL-DATA ANALYSIS; ENERGY-CONSUMPTION; TRANSPORTATION; EMISSIONS; MODELS;
D O I
10.1016/j.egypro.2016.09.047
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The long-term forecasting of electricity price has received less attention in literature. A probable reason for less attention is uncertainty about various factors in the long run, for instance- oil price, regulatory policies, political intervention, technological changes, energy mix, grid operations, etc. As a general operation, energy generated through different sources is supplied to grid which is finally composed of "energy mix". A large decision on the finalisation of retail electricity price could also depend on the load factors and capacity utilization of energy generating plants. A majority of the studies dealing with electricity price forecast electricity prices in the short run. Whereas, the aim of this study is to present a long-term perspective by introducing a methodology framework that consists of various parameters associated with the forecasting of electricity price in the long run. To the best of our understanding, this framework has not been proposed in existing energy literature, and therefore, under an assumption that future electricity market will be dominated by clean energy generation, this study brings novelty to the literature. (C) 2016 Published by Elsevier Ltd.
引用
收藏
页码:195 / 199
页数:5
相关论文
共 50 条
[41]   EPFG: Electricity Price Forecasting with Enhanced GANS Neural Network [J].
Hanif, Maria ;
Shahzad, Muhammad K. ;
Mehmood, Vaneeza ;
Saleem, Inshaal .
IETE JOURNAL OF RESEARCH, 2023, 69 (09) :6473-6482
[42]   An adaptive hybrid model for short term electricity price forecasting [J].
Zhang, Jinliang ;
Tan, Zhongfu ;
Wei, Yiming .
APPLIED ENERGY, 2020, 258
[43]   A Literature Review with Statistical Analysis of Electricity Price Forecasting Methods [J].
Cerjan, Marin ;
Krzelj, Ivana ;
Vidak, Marko ;
Delimar, Marko .
2013 IEEE EUROCON, 2013, :756-763
[44]   Electricity Price Forecasting Using Neural Network with Parameter Selection [J].
Ibrahim, Nik Nur Atira Nik ;
Razak, Intan Azmira Wan Abdul ;
Sidin, Siti Syakirah Mohd ;
Bohari, Zul Hasrizal .
INTELLIGENT AND INTERACTIVE COMPUTING, 2019, 67 :141-148
[45]   Impact of electricity price forecasting errors on bidding: a price-taker's perspective [J].
Zheng, Kedi ;
Wen, Bojian ;
Wang, Yi ;
Chen, Qixin .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2020, 14 (25) :6259-6266
[46]   Impact of Gas Price on Electricity Price Forecasting via Supervised Learning and Random Walk [J].
Poyrazoglu, Gokturk .
2019 16TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM), 2019,
[47]   Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes [J].
Liu, Shengyuan ;
Jiang, Yicheng ;
Lin, Zhenzhi ;
Wen, Fushuan ;
Ding, Yi ;
Yang, Li .
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2023, 11 (02) :523-533
[48]   From day-ahead to mid and long-term horizons with econometric electricity price forecasting models [J].
Ghelasi, Paul ;
Ziel, Florian .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2025, 217
[49]   Electricity prices forecasting by a hybrid evolutionary-adaptive methodology [J].
Osorio, G. J. ;
Matias, J. C. O. ;
Catalao, J. P. S. .
ENERGY CONVERSION AND MANAGEMENT, 2014, 80 :363-373
[50]   LOCALLY LINEAR NEURO-FUZZY (LLNF) ELECTRICITY PRICE FORECASTING IN DEREGULATED POWER MARKETS [J].
Abdollahzade, Majid ;
Mahjoob, Mohammad J. ;
Zarringhalam, Reza ;
Miranian, Arash .
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2010, 6 (09) :4203-4218