基于SEELM多专家模型的分布式光伏系统负荷预测方法

针对分布式光伏系统负荷所具有的非线性和非平稳等数据分布特性，基于神经网络与挂起规则，提出一种基于多模型集成式极限学习机的分布式光伏负荷预测方法。首先，设计多个神经网络作为子专家模型，并随机选取每一个网络的初始输入权值。构建挂起规则，依据数值波动范围在相应时间节点划分各神经网络的类别。针对其中数值波动较大的大误差网络，基于对应数值概率分布实施在线动态更新，以实现训练误差、输入权值的双维度同步优化。最后，将各个子专家模型的优化结果进行整合，并汇总输出，从而降低初始权值选取步骤中潜在误差波动的不利影响。基于某地区实际分布式光伏系统实施实证仿真，结果表明：在光伏负荷高波动这一特殊数据环境下，所提出预测模型在预测精度以及输出稳定性两方面均能够保持一定优势，可进一步推动并改善光伏接入背景下系统负荷预测的性能与效果。

An SEELM-based ensemble method for load forecasting in a distributed photovoltaic systems

Given the nonlinear and non-stationary data distribution characteristics of distributed photovoltaic system load, this paper proposes a suspended ensemble extreme learning machine (SEELM) method based on neural networks and a hanging criterion to implement power load prediction in distributed photovoltaic systems. First, multiple neural network models are built, and the initial input weights of each model are randomly assigned. Then the hanging criteria are designed to divide the models into two parts according to the numerical fluctuation ranges at different time spots. For large error models with larger fluctuation ranges, the online updates will be carried out in a probabilistic way to optimize the training error and input weights simultaneously. Finally, the outputs of all submodels are taken for the final output, which can reduce the error fluctuation impacts in the initial weight selection step. Based on an empirical simulation of the actual distributed photovoltaic system in a region, the advantages of the proposed method in terms of prediction accuracy and output stability under the scenarios of large fluctuation in photovoltaic load can be verified, and better capability and performance of load forecasting in the high-proportion photovoltaic systems can thus be achieved. This work is supported by the National Natural Science Foundation of China (No. 61803343).