Development of Neurofuzzy Architectures for Electricity Price Forecasting

• Main Authors: Abeer Alshejari, Vassilis S. Kodogiannis, Stavros Leonidis
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Energies
• Subjects: day-ahead electricity price forecasting; neurofuzzy systems; neural networks; clustering; prediction
• Online Access: https://www.mdpi.com/1996-1073/13/5/1209

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.