期刊
APPLIED ENERGY
卷 198, 期 -, 页码 203-222出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2017.04.039
关键词
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)
资金
- National Natural Science Foundation of China [61331007, 61471105]
- 973 Project [613273]
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 shortterm electricity price forecasting, respectively. (C) 2017 Elsevier Ltd. All rights reserved.
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