首页|基于最大重叠离散小波变换和深度学习的光伏功率预测

基于最大重叠离散小波变换和深度学习的光伏功率预测

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针对光伏功率时间序列的非平稳特性,提出一种基于最大重叠离散小波变换(MODWT)和长短期记忆网络(LSTM)的光伏功率组合预测模型.利用皮尔逊相关系数确定影响光伏功率的重要气象因素,基于MODWT算法对历史光伏功率序列进行分解,将选取的气象因素与分解得到的平稳子序列共同构成各个LSTM网络输入,通过汇总重构每个LSTM网络的子序列预测结果得到最终的光伏功率预测结果.从理论层面分析所建立的MODWT算法的完全重构性,并基于李雅普诺夫稳定性定理推导保证预测网络收敛的学习率范围.仿真对比结果显示,所提出的光伏功率预测模型在预测精度和鲁棒性方面具有明显优势.
PHOTOVOLTAIC POWER FORECASTING BASED ON MAXIMUM OVERLAP DISCRETE WAVELET TRANSFORM AND DEEP LEARNING
Aiming at the non-stationary characteristics of PV power time series,this paper proposes a hybrid PV power forecasting model based on maximum overlap discrete wavelet transform(MODWT)and long short-term memory network(LSTM).First,Pearson correlation coefficient is used to identify important meteorological factors while MODWT is used to decompose the historical PV power series.The selected meteorological factors and the decomposed stationary subsequences are combined to form the input of each LSTM network.The sub-sequence prediction results of each LSTM network are integrated and reconstructed to the final PV power prediction results.The complete reconstruction of MODWT algorithm established in this paper is analyzed at the theoretical level,and the range of learning rate to ensure the convergence of the prediction network is derived based on Lyapunov stability theorem.The simulation results show that this proposed forecasting model has the obvious advantages in forecasting accuracy and robustness.

photovoltaic power forecastinglong short-term memory networknon-stationary time series decompositionconvergence of prediction network

马乐乐、孔小兵、郭磊、刘源延、刘向杰

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华北电力大学控制与计算机工程学院,北京 102206

光伏功率预测 长短期记忆网络 非平稳时间序列分解 预测网络收敛性

国家重点研发计划国家自然科学基金国家自然科学基金中国博士后科学基金

2021YFE019090062073136622031702022T150210

2024

10.19912/j.0254-0096.tynxb.2022-1993

太阳能学报
中国可再生能源学会

太阳能学报

CSTPCD北大核心
影响因子：0.392
ISSN：0254-0096
年,卷(期)：2024.45(5)
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