基于改进的极限学习机光伏出力短期预测
摘要：针对传统极限学习机易陷入局部最优解的缺点以及环境变化导致光伏出力波动的特点，构建了一种基于自适应噪声完全集成经验模态分解（complete ensemble empirical mode decomposition with adaptive noise，CEEMDAN）算法，结合黑猩猩优化算法优化极限学习机神经网络的光伏出力短期预测模型。首先利用CEEMDAN算法将影响光伏输出功率的关键环境因素序列进行分解，得到数据信号在不同时间尺度的局部特征，降低环境因素序列的非平稳性，然后将各分解子序列和光伏历史数据序列作为黑猩猩算法优化的极限学习机预测模型输入进行预测。最后，选用DKASC Solar Centre光伏电站数据集对不同预测模型进行验证对比。实例仿真结果表明，构建的改进光伏出力预测组合模型的各项指标预测效果更好，且适用不同环境的光伏发电预测。
Abstract:In allusion to the defect of traditional extreme learning machine easily falling into local optimal solutions and the characteristic of environment variation leading to the fluctuation of photovoltaic (abbr. PV) output, based on complete ensemble empirical mode decomposition with adaptive noise (abbr. CEEMDAN) algorithm and combining with extreme learning machine neural network optimized by chimp optimization algorithm a short-term PV output prediction model was constructed. Firstly, by use of CEEMDAN algorithm the key environment factor series impacting PV output was decomposed to obtain the local feature of data signals in different time-scales to reduce the non-stationary of environment factor series. Secondly, taking each decomposed subseries and PV historical data series as the input of extreme learning machine prediction model optimized by the chimp algorithm the prediction was performed. Finally, the data set of DKASC Solar Centre PV station was chosen to conduct the contrast and verifying for different prediction models. Results of simulation example show that the prediction effect of each index of the constructed improved PV output prediction combined model is better and suitable to the prediction of PV generation in different environments.
标题：基于改进的极限学习机光伏出力短期预测
英文标题：Short-term Prediction of Photovoltaic Output Based on Improved Extreme Learning Machines
