基于灰色极限学习机组合光伏发电预测

Prediction of Combined Photovoltaic Power Generation Based on Gray Extreme Learning Machine

随着全球能源消耗量的急剧上升,光伏发电由于成本低,环境污染小,得到了大力推广,目前已成为全球能源供给的主体、并且有广泛的应用范围,属于新型情节能源。然而,光伏电站当前在短期功率预测方面依然不太准确,通过利用灰色模型、极限学习机相互组合,形成全新的评估预测方法。方法需要分成两个板块的程序设计,即灰色GM(1,1)模型、极限学习机模型,以此实现数据录入、灰色GM模型预测、极限学习机模型预测以及灰色极限学习机光伏发电功率的预测。最后分别计算灰色模型和极限学习机模型预测结果的平均绝对百分数(MAPE)误差,按照MAPE的大小分配权重并重新组合,得到组合预测结果。研究证明,该方法相对单一的灰色GM(1,1)模型、极限学习机预测而言,在预测精准度上更高,并且整体预测结果符合真实水平。

With the sharp increase in global energy consumption,photovoltaic power generation has been vigorously promoted due to its low cost and low environmental pollution.At present,it has become the main source of global energy supply and the most promising clean energy source.Aiming at the problem of the short-term power prediction accuracy of photovoltaic power stations,a gray extreme learning machine combination prediction method combining a gray model and an extreme learning machine is proposed.In this method,the programming of the gray GM(1,1)model and the extreme learning machine model were implemented to realize the data reading,the gray GM(1,1)model prediction,the extreme learning machine model prediction,and the gray extreme learning machine photovoltaic Forecast of generated power.Finally,the average absolute percentage(MAPE)errors of the prediction results of the gray model and the extreme learning machine model are calculated,and the weights are allocated according to the size of the MAPE and recombined to obtain the combined prediction result.The study proves that this method is more accurate than the single grey GM(1,1)model and extreme learning machine prediction,and the overall prediction results are in line with the real level.