A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. (15th March 2015)

• Record Type: Journal Article
• Authors: Xiao, Liye; Wang, Jianzhou; Hou, Ru; Wu, Jie
• Is Part Of: Energy. Volume 82(2015)
• Page Start: 524
• Page End: 549
• Publication Date: 2015-03-15
• Subjects: Electrical power forecasting -- Combined model -- Forecasting accuracy -- Data pre-analysis; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.01.063
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Electrical load forecasting has always played a key role in power system administration, planning for energy transfer scheduling and load dispatch. For electrical load forecasting, due to the fact that combined model has the capacity to effectively calculate the seasonality and nonlinearity shown in the electrical load data, absorb the merits and avoid the limitations of the individual models, a new combined model is presented. In this model, the data pre-analysis is used to reduce the interferences from the data, meanwhile cuckoo search is firstly used to optimize weight coefficients of the combined model. To evaluate the forecast performance of the proposed combined model, half-hourly electricity power data from February 2006 to 2009 for the State of New South Wales, August 2006 to 2008 for the State of Victoria and November 2006 to 2008 for the State of Queensland, Australia, were used in this paper as a case study. The experimental results show that the proposed combined model is superior to the individual forecasting models regarding forecast performance. Highlights: Improve the accuracy and stability significantly of electrical power forecasting. Propose a combined model based on several artificial neural networks. Pre-analyze the data before prediction. Apply cuckoo search algorithm to optimize the weight coefficients.