基于组合损失函数的BP神经网络风力发电短期预测方法

为了改善风电大规模并网带来的电力系统功率平衡问题，提高系统的风电消纳能力，构建了基于组合损失函数的风电功率预测神经网络模型. 为了提高原始数据信息的利用率，在模型中将数据进行分类，提出以最小化组合损失函数为目标的BP神经网络风力发电短期预测模型，由均方差损失函数、交叉熵损失函数和排序损失函数按照不同的权重比构成组合损失函数. 基于实际风场数据，对基于组合损失函数的预测模型效果进行训练和仿真验证，结果表明相较于基于单一的均方差损失函数的预测方法，提出的组合损失函数可有效提高预测精度.

Short-term forecasting method of wind power generation based on BP neural network with combined loss function

A short-term forecasting model of neural network for wind power generation with the combined loss function was proposed, in order to reduce the side effect of large-scale wind power integration on power system energy balance and increase system’s wind power accommodation ability. A classification method was introduced into the model, and a BP neural network short-term wind power prediction model with the goal of minimizing the combined loss function was proposed, in order to improve the utilization of raw data information. The combined loss function was constructed by the mean square error loss function, the cross-entropy loss function and the rank loss function according to different weight ratios. Compare to the prediction method based on separate loss functions, the combined loss function proposed can effectively improve the prediction accuracy from real wind farm data test.