کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
764659 | 896995 | 2010 | 8 صفحه PDF | دانلود رایگان |
This paper proposes a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED), an Enhanced Probability Neural Network (EPNN) is proposed in the solving process. In this paper, the Locational Marginal Price (LMP), system load and temperature of PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday, and weekend. With the OED to smooth parameters in the EPNN, the forecasting error can be improved during the training process to promote the accuracy and reliability where even the “spikes” can be tracked closely. Simulation results show the effectiveness of the proposed EPNN to provide quality information in a price volatile environment.
Journal: Energy Conversion and Management - Volume 51, Issue 12, December 2010, Pages 2707–2714