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Energies 2016, 9(10), 804; doi:10.3390/en9100804

Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market

Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China
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Academic Editor: Javier Contreras
Received: 16 June 2016 / Revised: 18 September 2016 / Accepted: 27 September 2016 / Published: 8 October 2016
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
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Abstract

For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the traditional GM(0, N) model, three major improvements of the proposed model are: (i) the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii) a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii) the model parameters are identified by an improved particle swarm optimization (PSO) instead of the least square method (LSM) for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR) model, the traditional GM(0, N) model and the artificial neural network (ANN) model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM) test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market. View Full-Text
Keywords: market clearing price (MCP); mid-term; grey prediction model; sparse data; particle swarm optimization (PSO) market clearing price (MCP); mid-term; grey prediction model; sparse data; particle swarm optimization (PSO)
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Cheng, C.; Luo, B.; Miao, S.; Wu, X. Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market. Energies 2016, 9, 804.

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