基于时序分解与误差修正的新能源爬坡事件预测  

New energy ramp event prediction based on time series decomposition and error correction

作　　者：童林 官铮[2] 王立威[1] 杨文韬[1] 姚洋 TONG Lin;GUAN Zheng;WANG Li-wei;YANG Wen-tao;YAO Yang(School of Physics and Electrical Engineering,Liupanshui Normal University,Liupanshui 553004,China;School of Information Engineering,Yunnan University,Kunming 650091,China)

机构地区：[1]六盘水师范学院物理与电气工程学院,贵州六盘水553004 [2]云南大学信息学院,云南昆明650091

出　　处：《浙江大学学报（工学版）》2022年第2期338-346,共9页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61761045);贵州省教育厅资助项目(黔教合KY字[2022]051号);六盘水师范学院校级资助项目(LPSSYSSDPY201704,LSZDZY2018-03)。

摘　　要：为了提高以风电、光伏为代表的新能源的爬坡预测的准确性,提出基于主成分分析、时序分解与修正长短期记忆(LSTM)网络预测误差的爬坡预测模型.为了充分考虑功率的时序特性,采用时序分解方法将功率分解为周期、趋势和余项,结合多个特征因素的主成分建立基于LSTM的趋势和余项预测模型,实现功率的时间特征与影响因素主成分的映射关系刻画.在采用LSTM对趋势和余项进行初步预测的基础上,引入误差修正算法计算拟合预测模型的动态误差并构建新的非平稳时间序列,获得准度性更佳的趋势和余项预测值.通过加法模型融合趋势、余项以及利用朴素法获得的周期,得到最终预测功率.结合风电和光伏爬坡事件定义,运用所提模型分别进行风电和光伏爬坡预测.实验结果表明,与其他预测方法相比,所提模型在功率直接预测和爬坡事件间接预测上均具有更优的精度,能够为电网调度提供更可靠的依据.A ramp prediction model based on principal component analysis, time series decomposition and correction of long short-term memory(LSTM) network was proposed, in order to improve the accuracy of ramp prediction of new energy represented by wind power and photovoltaic power. In order to fully consider the time series characteristics of power, the power was decomposed into period, trend and residual by the time series decomposition method, and the trend and residual prediction model based on LSTM was established by combining the principal components of several characteristic factors, to realize the mapping relationship between the time characteristics of power and the principal components of influencing factors. Based on the preliminary prediction of trend and residual terms by LSTM, an error correction algorithm was introduced to calculate the dynamic error of the fitting prediction model and construct a new non-stationary time series to obtain the trend and residual predicted values with better accuracy. The final power prediction was obtained by fusing the trend, residual terms and the period value obtained by using the naive method. Combined with the definition of wind power and photovoltaic ramp event, the proposed model was used to predict the wind power and photovoltaic ramp event respectively. Experimental results show that the proposed model has better accuracy than other forecasting methods in direct power prediction and indirect ramp event prediction, and it can provide a more reliable basis for power grid dispatching.

关 键 词：主成分分析 长短期记忆网络 误差修正 新能源爬坡 时序分解 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]