浙江电力2024，Vol.43Issue(8)：85-93,9.DOI:10.19585/j.zjdl.202408010
基于改进灰狼算法和TCN-QRF的超短期光伏出力概率预测
Probabilistic forecasting of ultra-short-term PV output using the improved GWO and TCN-QRF
摘要
Abstract
As the share of photovoltaic(PV)power generation grows increasingly within electric power systems,the accurate probabilistic forecasting of PV output can be help for grid regulation and operation.To enhance forecasting precision,a probabilistic forecasting method for ultra-short-term photovoltaic output using the improved grey wolf optimization(GWO),temporal convolutional neural networks and quantile random forests(TCN-QRF)is proposed.Firstly,the preprocessed time series dataset for PV output is converted into a supervised learning dataset.Then,the TCN is used to extract the temporal features of PV output as the input to the QRF,constructing the TCN-QRF model.Finally,the GWO is improved using the nonlinear convergence factor and Gaussian mutation strategy.The improved GWO efficiently selects hyperparameters for TCN-QRF,enabling a more precise probabilistic forecasting of PV output.关键词
改进灰狼算法/概率预测/光伏出力/TCN-QRFKey words
improved GWO/probabilistic forecasting/PV output/TCN-QRF引用本文复制引用
朱涛,杨欢红,肖峰,李光,李广一,朱伟星,叶婧元..基于改进灰狼算法和TCN-QRF的超短期光伏出力概率预测[J].浙江电力,2024,43(8):85-93,9.基金项目
国家自然科学基金(52177100) （52177100）
中国华电集团公司科技项目(CHDKJ23-02-233) （CHDKJ23-02-233）
