Performance of a Hybrid Neural-Based Framework for Alternative Electricity Price Forecasting in the Smart Grid

  • Gang Lei School of Mechatronics & Vehicle Engineering, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China
  • Chunxiang Xu Civil Engineering College, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China
  • Junmin Chen School of Mechatronics & Vehicle Engineering, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China
  • Hongyang Zhao School of Mechatronics & Vehicle Engineering, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China
  • Hesam Parvaneh Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Keywords: electricity price forecasting; artificial neural networks; smart grids; electricity markets


Electricity forecasting is an essential task for energy management systems of microgrids deployed in smart grids. Accurate price forecasting will eventually enhance the economic operation of microgrids. In this regard, the literature is rich with studies focused on predicting electricity price data using artificial neural networks. However, most of them consider a single model such as multi-layer perceptron (MLP) and radial basis function (RBF) to perform electricity price forecasting. In this paper, a hybrid framework based on simultaneously utilizing MLP-RBF neural networks is presented to predict the Iranian electricity market price. In addition, few works in literature considered Iran’s electricity market as their case of analysis and investigation. Forecasting results indicate that MLP neural networks outperform the RBF neural networks. The values for the coefficient of determination (R) corresponding to MLP and RBF neural networks are obtained 0.55 and 0.44, respectively. However, the proposed hybrid framework performed better than both MLP and RBF models with R-value equal to 0.71. In addition to this, the MSE and RMSE values show the superiority of the proposed method to the single methods.


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Author Biographies

Gang Lei, School of Mechatronics & Vehicle Engineering, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China

Gang Lei, associate professor, master of electronic and communication engineering major, Zhengzhou University, teaches at electrical engineering and intelligent control major, department of electromechanical and vehicle engineering, Zhengzhou University of Technology, research direction: research on smart power supply and smart grid.

Chunxiang Xu, Civil Engineering College, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China

Chunxiang Xu, Professor of Zhengzhou University of Technology, was born in February 1968. Bachelor degree of electrical automation major, China University of Mining and Technology, master of control theory and engineering major, Zhengzhou University, she engaged in the application and research of electrical automatic control and electronic technology. She has presided over and completed more than ten provincial projects, a number of municipal or bureau level projects, she has published more than 40 papers, edited or participated in edit 12 textbooks.

Junmin Chen, School of Mechatronics & Vehicle Engineering, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China

Junmin Chen, professor level senior engineer, has been engaged in the research and development of mobile power stations for a long time. He has developed more than 20 products to support China’s new missile weapons, and he has participated in large-scale national military parades for many times; he has more than 20 patents. He published 9 academic papers in the national core journal of Mobile Power Station and Vehicle, and compiled and published the textbook of Practical Electrotechnical Measurement Technology as the chief editor. He has participated in the drafting, examination and approval of national standards and industrial standards, such as Reciprocating Internal Combustion Engine Driven Alternating Current Generating Sets (GB/T 2820.6); Medium/High-Power Mine Gas Generating Set (GB/T 29487); Medium/High-Power Biogas Generating Set(GB/T 29488), etc. At present, he is mainly engaged in electrical engineering and automation teaching.

Hongyang Zhao, School of Mechatronics & Vehicle Engineering, Zhengzhou University of Technology, Zhengzhou, Henan, 450044, China

Hongyang Zhao, bachelor degree, major in electrical engineering and intelligent control. Research direction: intelligent power supply.

Hesam Parvaneh, Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

Hesam Parvaneh is affiliated with Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. He is also designer and supervisor in south of Kerman electric power distribution company. His research interests are distribution system, power electronic, optimization, renewable energy, dynamic of power system.


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