基于时序分解及机器学习的风电功率组合预测模型
摘要：精准的风电功率预测结果可保障电网在安全稳定运行条件下提高风电并网容量。为提高风电功率预测精度，融合时间序列分解技术、机器学习及启发式算法提出一种风电功率双层组合预测模型。首先，构建经验模态分解技术和长短期记忆神经网络相结合（empirical mode decomposition combined with long short term memory network, EMD-LSTM）的预测模型。同时，构建变分模态分解技术、模拟退火算法及深度置信网络相结合（variational mode decomposition, simulated annealing combined with deep belief network，VMD-SA-DBN）的预测模型。并将已构建的EMD-LSTM及VMD-SA-DBN模型作为组合预测模型上层的基础预测模型。其次，利用极端梯度提升算法构建下层预测模型，并将上层2个基础预测模型的预测结果输入到下层预测模型，以得到最终的预测结果。最后，利用实测数据对此算法的有效性进行验证。证明所提出的双层组合预测模型较单一预测模型具有更高的预测精度。
Abstract:Accurate wind power prediction results can improve the grid-connected capacity of wind power under the stable and secure operation of power grid. To improve the prediction accuracy of wind power, by means of integrating time series decomposition technology, machine learning and heuristic algorithm a dual-level combined prediction model for wind power was proposed. Firstly, a prediction model combining empirical mode decomposition technology with long- and short- term memory network (abbr. EMD-LSTM) was constructed. Meanwhile, a prediction model, in which the variational mode decomposition and simulated annealing algorithm (abbr. VMD-SA) were combined with deep belief network (abbr. DBN), was proposed. The constructed EMD-LSTM model and VMD-SA-DBN model were taken as the basic prediction models of the upper layer of the combined prediction model. Secondly, the extreme gradient boosting algorithm was used to construct the lower layer of the combined prediction model, and the prediction result from the two basic prediction model in the upper layer was input into the lower prediction model, to obtain the final prediction result. Finally, the effectiveness of the proposed algorithm was verified by measured data. Verification result shows that the prediction accuracy by the proposed two layer combined prediction model is higher than that from the single prediction model.
标题：基于时序分解及机器学习的风电功率组合预测模型
英文标题：Wind Power Combination Prediction Model Based on Time Series Decomposition and Machine Learning
作者：赵冬梅, 杜刚, 刘鑫, 吴志强, 李超,
关键词：风电功率预测, 经验模态分解, 变分模态分解, 长短期记忆神经网络, 深度置信网络, 极端梯度提升,
发表日期：2022-01-10
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