Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study

光伏系统 计算机科学 随机森林 粒子群优化 反向传播 人工神经网络 人工智能 机器学习 工程类 电气工程
作者
Dongxiao Niu,Keke Wang,Lijie Sun,Jing Wu,Xiaomin Xu
出处
期刊:Applied Soft Computing [Elsevier BV]
卷期号:93: 106389-106389 被引量:173
标识
DOI:10.1016/j.asoc.2020.106389
摘要

To mitigate solar curtailment caused by large-scale development of photovoltaic (PV) power generation, accurate forecasting of PV power generation is important. A hybrid forecasting model was constructed that combines random forest (RF), improved grey ideal value approximation (IGIVA), complementary ensemble empirical mode decomposition (CEEMD), the particle swarm optimization algorithm based on dynamic inertia factor (DIFPSO), and backpropagation neural network (BPNN), called RF-CEEMD-DIFPSO-BPNN. PV power generation is affected by many factors. The RF method is used to calculate the importance degree and rank the factors, then eliminate the less important factors. Then, the importance degree calculated by RF is transferred as the weight values to the IGIVA model to screen the similar days of different weather types to improve the data quality of the training sets. Then, the original power sequence is decomposed into intrinsic mode functions (IMFs) at different frequencies and a residual component by CEEMD to weaken the fluctuation of the original sequence. We empirically analyzed a PV power plant to verify the effectiveness of the hybrid model, which proved that the RF-CEEMD-DIFPSO-BPNN is a promising approach in terms of PV power generation forecasting.
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