A New Prediction Approach Using FCM and Deep Cascade Elastic Broad Learning for Photovoltaic Power Generation in Short Term

Zhaoyang Wei, Wu Deng, Huimin Zhao, Jiameng Xue
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Abstract

In recent years, photovoltaic power generation prediction has mainly faced some difficulties, such as low prediction accuracy, time-consuming model training, and so on. In order to improve the prediction accuracy of photovoltaic power generation in short term, it is greatly beneficial to optimize power allocating plans and improve economic benefits. Therefore, a new prediction method of Photovoltaic (PV) power generation based on Fuzzy C-Means (FCM) and deep cascade elastic broad learning is proposed in this paper. Firstly, the fuzzy c-means clustering algorithm is used to classify the input factors. Then, the feature mapping nodes of the broad learning model are constructed by the deep cascade method to extract the features of the input factors fully. The elastic net regularization method is employed to constrain the network output weight, measure the influence of the nodes on the weight, extract the more influential nodes, and sparse network structure. A deep cascade elastic broad learning network is established for short-term photovoltaic power prediction. Finally, the effectiveness of the method is verified through the original data. The experimental results verify the model can better predict the actual photovoltaic power generation and has a good application value.
基于FCM和深度级联弹性广义学习的光伏发电短期预测新方法
近年来,光伏发电预测主要面临着预测精度低、模型训练耗时等困难。为了提高光伏发电短期预测精度,对优化电力分配方案,提高经济效益有很大的好处。为此,本文提出了一种基于模糊c均值(FCM)和深度级联弹性广义学习的光伏发电预测新方法。首先,采用模糊c均值聚类算法对输入因子进行分类;然后,采用深度级联方法构建广义学习模型的特征映射节点,充分提取输入因素的特征;采用弹性网络正则化方法约束网络输出权值,度量节点对权值的影响,提取影响较大的节点,稀疏网络结构。建立了一个深度级联弹性广义学习网络,用于光伏短期功率预测。最后,通过原始数据验证了该方法的有效性。实验结果验证了该模型能较好地预测实际光伏发电量,具有较好的应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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