| 引用本文: | 张静,褚晓红,黄学安,等.一种基于加权马尔科夫链修正的SVM光伏出力预测模型[J].电力系统保护与控制,2019,47(19):63-68.[点击复制] |
| ZHANG Jing,CHU Xiaohong,HUANG Xue'an,et al.A model for photovoltaic output prediction based on SVM modified by weighted Markov chain[J].Power System Protection and Control,2019,47(19):63-68[点击复制] |
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| 摘要: |
| 构建高效的光伏出力预测模型，能减少光伏出力随机性对电力系统的冲击。考虑光伏发电的随机性和不稳定性，提出用加权的马尔科夫链修正SVM预测模型，以提高预测精度。首先建立SVM光伏出力预测模型，预测未来1天的出力曲线。然后基于均值-均方差方法对预测残差进行分级，以残差序列标准化的各阶自相关系数为权重，运用加权马尔科夫链模型，预测残差的未来状态。最后根据未来状态空间的阈值对SVM预测结果进行修正。将此模型应用到某光伏发电系统的出力预测实例中，仿真结果表明，修正后的模型预测精度更高，模型具备可行性和有效性。 |
| 关键词: 光伏系统 SVM 加权马尔科夫链 出力预测 残差修正 |
| DOI：10.19783/j.cnki.pspc.181394 |
| 投稿时间：2018-11-08修订日期：2019-05-23 |
| 基金项目:国家自然科学基金项目资助(11601125) |
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| A model for photovoltaic output prediction based on SVM modified by weighted Markov chain |
| ZHANG Jing,CHU Xiaohong,HUANG Xue'an,FAN Wen,CHEN Yan,WAN Quan,ZHAO Jiakui |
| (Beijing China-Power Information Technology Co., Ltd.State Grid Information & Telecommunication Group, Beijing 100192, China;State Grid Tongling Electric Power Supply Company, Tongling 244000, China) |
| Abstract: |
| Building an efficient PV output forecasting model can reduce the impact of PV output fluctuation on the power system. Considering the randomness and instability of photovoltaic power generation, a weighted Markov chain is proposed to modify the SVM prediction model to improve the prediction accuracy. Firstly, the SVM photovoltaic output prediction model is established to predict the output curve of the next day. Then, the relative errors are classified based on mean-mean square deviation method, and the weighted Markov chain model is used to predict the future state of the relative errors by weighting the autocorrelation of each order normalized error sequence. The threshold of state space is used to correct the prediction result of SVM. The model is applied to the output prediction of a photovoltaic power system. The simulation results show that the model modified by weighted Markov chain has higher prediction accuracy, and the model is feasible and effective. This work is supported by National Natural Science Foundation of China (No. 11601125). |
| Key words: photovoltaic system SVM weighted Markov chain power forecasting error correction |
