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The price prediction for the energy market based on a new method

Author

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  • Homayoun Ebrahimian
  • Saeed Barmayoon
  • Mohsen Mohammadi
  • Noradin Ghadimi

Abstract

Regarding the complex behaviour of price signalling, its prediction is difficult, where an accurate forecasting can play an important role in electricity markets. In this paper, a feature selection based on mutual information is implemented for day ahead prediction of electricity prices, which are so valuable for determining the redundancy and relevancy of selected features. A combination of wavelet transform (WT) and a hybrid forecast method is presented based on a neural network (NN). Furthermore, an intelligent algorithm is considered for a prediction process to set the proposed forecast engine free parameters based NN. This optimisation process improved the accuracy of the proposed model. To demonstrate the validity of this model, the Pennsylvania-New Jersey-Maryland (PJM) electricity market is considered as a test case and compared with some of the most recent price forecast methods. These comparisons illustrate the effectiveness of the proposed strategy.

Suggested Citation

  • Homayoun Ebrahimian & Saeed Barmayoon & Mohsen Mohammadi & Noradin Ghadimi, 2018. "The price prediction for the energy market based on a new method," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 31(1), pages 313-337, January.
    • Handle: RePEc:taf:reroxx:v:31:y:2018:i:1:p:313-337
    DOI: 10.1080/1331677X.2018.1429291
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    Cited by:

    1. Zhang, Bo & Wang, Rongjie & Jiang, Desong & Wang, Yichun & lin, Anhui & Wang, Jianfeng & Ruan, Bingcong, 2023. "Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm," Energy, Elsevier, vol. 283(C).
    2. Chai, Shanglei & Li, Qiang & Abedin, Mohammad Zoynul & Lucey, Brian M., 2024. "Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives," Research in International Business and Finance, Elsevier, vol. 67(PA).
    3. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).

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