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
收藏
页码:299 / 304
页数:6
相关论文
共 50 条
  • [41] A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting
    Hadwan, Mohammad
    Al-Maqaleh, Basheer M.
    Al-Badani, Fuad N.
    Khan, Rehan Ullah
    Al-Hagery, Mohammed A.
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 70 (03): : 4829 - 4845
  • [42] A hybrid neural network and ARIMA model for water quality time series prediction
    Faruk, Durdu Oemer
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2010, 23 (04) : 586 - 594
  • [43] Application of Time-series and Artificial Neural Network Models in Short Term Load Forecasting for Scheduling of Storage Devices
    Ahmed, K. M. U.
    Ampatzis, M.
    Nguyen, P. H.
    Kling, W. L.
    2014 49TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC), 2014,
  • [44] A hybrid day-ahead electricity price forecasting framework based on time series
    Xiong, Xiaoping
    Qing, Guohua
    ENERGY, 2023, 264
  • [45] Electricity Price Forecasting for Norwegian Day-Ahead Market using Hybrid AI Models
    Vamathevan, Gajanthini
    Dynge, Marthe Fogstad
    Cali, Umit
    2022 18TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM, 2022,
  • [46] A Bayesian regularized artificial neural network for stock market forecasting
    Ticknor, Jonathan L.
    EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (14) : 5501 - 5506
  • [47] Stock Market Forecasting Based on Artificial Neural Network Model
    Zhou Shaofu
    Xu Yang
    RECENT ADVANCE IN STATISTICS APPLICATION AND RELATED AREAS, PTS 1 AND 2, 2008, : 1119 - 1123
  • [48] Predicting Computer Network Traffic: A Time Series Forecasting Approach using DWT, ARIMA and RNN
    Madan, Rishabh
    Mangipudi, Partha Sarathi
    2018 ELEVENTH INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING (IC3), 2018, : 1 - 5
  • [49] Investigation of Day-ahead Price Forecasting Models in the Finnish Electricity Market
    Zaroni, Daniel
    Piazzi, Arthur
    Tettamanti, Tamas
    Sleisz, Adam
    ICAART: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2, 2020, : 829 - 835
  • [50] Red tide time series forecasting by combining ARIMA and deep belief network
    Qin, Mengjiao
    Li, Zhihang
    Du, Zhenhong
    KNOWLEDGE-BASED SYSTEMS, 2017, 125 : 39 - 52