Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

被引：0

作者：

Areekul, Phatchakorn ^{[1
]}

Senjyu, Tomonobu ^{[1
]}

Urasaki, Naomitsu ^{[1
]}

Yona, Atsushi ^{[1
]}

机构：

[1] Univ Ryukyus, Fac Engn, Dept Elect & Elect Engn, Okinawa 9030213, Japan

来源：

ICIEA 2010: PROCEEDINGS OF THE 5TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOL 3 | 2010年

关键词：

Electricity price forecasting; neural network; ARIMA; combination methodology; back-propagation;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

引用

页码：299 / 304

页数：6

共 50 条

[31] A comparison of artificial neural network and time series models for forecasting commodity prices
Export Development Bank of Iran, 129 Khaled Eslamboli St., Tehran, 15139, Iran
不详
不详
NEUROCOMPUTING, 2 (169-181):
[32] Time series forecasting model using a hybrid ARIMA and neural network
Zou, Haofei
Yang, Fangfing
Xia, Guoping
PROCEEDINGS OF THE 2005 CONFERENCE OF SYSTEM DYNAMICS AND MANAGEMENT SCIENCE, VOL 2: SUSTAINABLE DEVELOPMENT OF ASIA PACIFIC, 2005, : 934 - 939
[33] Time series forecasting using a hybrid ARIMA and neural network model
Zhang, GP
NEUROCOMPUTING, 2003, 50 : 159 - 175
[34] One Day-ahead Price Forecasting for Electricity Market of Iran using Combined Time Series and Neural Network Model
Azadeh, A.
Ghadrei, S. F.
Nokhandan, B. Pourvalikhan
HIMA: 2009 IEEE WORKSHOP ON HYBRID INTELLIGENT MODELS AND APPLICATIONS, 2009, : 44 - 47
[35] Time Series Forecasting Using Artificial Neural Network
Varysova, Tereza
INNOVATION VISION 2020: FROM REGIONAL DEVELOPMENT SUSTAINABILITY TO GLOBAL ECONOMIC GROWTH, VOL I-VI, 2015, : 527 - 535
[36] TIME SERIES ARIMA BASED FORECASTING OF RICE PRICE
Garov, Arun Kumar
Awasthi, A. K.
INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 2023, 19 (01): : 285 - 303
[37] Day-ahead electricity price forecasting via the application of artificial neural network based models
Panapakidis, Ioannis P.
Dagoumas, Athanasios S.
APPLIED ENERGY, 2016, 172 : 132 - 151
[38] Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
Nascimento, Joao
Pinto, Tiago
Vale, Zita
2019 IEEE MILAN POWERTECH, 2019,
[39] Forecasting next-day electricity prices by time series models
Nogales, FJ
Contreras, J
Conejo, AJ
Espínola, R
IEEE TRANSACTIONS ON POWER SYSTEMS, 2002, 17 (02) : 342 - 348
[40] Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks
Camelo, Henrique do Nascimento
Lucio, Paulo Sergio
Vercosa Leal Junior, Joao Bosco
Marques De Carvalho, Paulo Cesar
dos Santos, Daniel von Glehn
ENERGY, 2018, 151 : 347 - 357

← 1 2 3 4 5 →