Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting. (15th July 2017)

• Record Type: Journal Article
• Authors: Xiao, Liye; Shao, Wei; Yu, Mengxia; Ma, Jing; Jin, Congjun
• Is Part Of: Applied energy. Volume 198(2017)
• Page Start: 203
• Page End: 222
• Publication Date: 2017-07-15
• Subjects: Electrical power system -- Hybrid model -- Improved cuckoo search algorithm -- Short-term electricity price forecasting (STEPF) -- Short-term load forecasting (STLF) -- Short-term wind speed forecasting (STWSF); Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.04.039
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Propose a hybrid model that can be used to forecast the complex electrical power system. Enhance the speed of local convergence and the accuracy of finding the optimal solution of CS. Use more accurate metrics to assess the forecasting performance of the proposed model. Abstract: Electricity forecasting plays an important role in the operation of electrical power systems. Many models have been developed to obtain accurate forecasting results, but most of them focus more on a single forecasting indicator, such as short-term load forecasting (STLF), short-term wind speed forecasting (STWSF) or short-term electricity price forecasting (STEPF). In this paper a new hybrid model based on the singular spectrum analysis (SSA) and modified wavelet neural network (WNN) is proposed for all the short-term load forecasting, short-term wind speed forecasting and short-term electricity price forecasting. In this model, a new improved cuckoo search (CS) algorithm is proposed to optimize the initial weights and the parameters of dilation and translation in WNN. Case studies of half-hourly electrical load data, 10-min-ahead wind speed data and half-hourly electricity price data are applied as illustrative examples to evaluate the proposed hybrid model, respectively. Experiments show that the hybrid model resulted in 46.4235%, 31.6268% and 25.8776% reduction in the mean absolute percentage error compared to the comparison models in short-term load forecasting, short-term wind speed forecasting and short-term electricity price forecasting, respectively.