Development of Neurofuzzy Architectures for Electricity Price Forecasting

Abeer Alshejari, Vassilis S. Kodogiannis and Stavros Leonidis
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Abeer Alshejari: Department of Mathematical Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Vassilis S. Kodogiannis: School of Computer Science & Engineering, University of Westminster, London W1W 6UW, UK
Stavros Leonidis: School of Pure and Applied Sciences, Open University of Cyprus, 2220 Nicosia, Cyprus

Energies, 2020, vol. 13, issue 5, 1-24

Abstract: In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision-making process as well as strategic planning. In this study, a prototype asymmetric-based neuro-fuzzy network (AGFINN) architecture has been implemented for short-term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well-established learning-based models.

Keywords: day-ahead electricity price forecasting; neurofuzzy systems; neural networks; clustering; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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