School of Computer Science and Engineering & School of Software, Guangxi Normal University, Guilin 541004, China.
Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin 541004, China.
Comput Intell Neurosci. 2022 Sep 22;2022:6486876. doi: 10.1155/2022/6486876. eCollection 2022.
Photovoltaic power generation is greatly affected by weather factors. To improve the prediction accuracy of photovoltaic power generation, complete ensemble empirical mode decomposition with an adaptive noise algorithm (CEEMDAN) is proposed to preprocess the power sequence. Then, the full convolutional network (FCN) model optimized based on the sparrow search algorithm (SSA) is used to predict the short-term photovoltaic power. SSA can more reasonably determine the parameters of FCN and improve the prediction performance of FCN. Therefore, the FCN model optimized by the SSA algorithm is used to establish prediction models for subsequences and predict each subsequence, respectively. Finally, the predicted value of each subsequence is superimposed. Taking the actual data of a photovoltaic power station in Jiangsu province of China as an example, by comparing some different common prediction models, it is proved that the proposed method is reasonable and feasible.
光伏发电受天气因素影响较大。为提高光伏发电预测精度，提出了一种基于自适应噪声的完备集合经验模态分解算法（CEEMDAN）对功率序列进行预处理。然后，使用基于麻雀搜索算法（SSA）优化的全卷积网络（FCN）模型对短期光伏发电进行预测。SSA 可以更合理地确定 FCN 的参数，从而提高 FCN 的预测性能。因此，使用 SSA 算法优化的 FCN 模型用于建立子序列的预测模型，并分别对每个子序列进行预测。最后，对每个子序列的预测值进行叠加。以中国江苏省某光伏电站的实际数据为例，通过与一些不同的常见预测模型进行比较，验证了所提方法的合理性和可行性。
