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首页> 外文期刊>Energy >Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification
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Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification

机译：基于神经网络的光伏发电容量预测系统，具有面向福利的修改

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摘要

Photovoltaic (PV) generation prediction is a critical technology for integrating solar energy in power systems and markets. Accuracy is the target for most PV prediction models, which represents the minimisation of the average error. However, minimisation of prediction error is to obtain a minimum cost from impact of prediction inaccuracy. The lowest average error may not always relate to the minimum cost. Thus, this paper proposes an integrated PV prediction structure that targets minimum industrial cost from prediction error other than using pure accuracy. The object of machine learning model is modified into the further industrial cost of prediction error, which is the cost of backup generation participation in power dispatch for power grid energy balancing. A feed-forward neural network is selected as typical machine learning model for integration. Additionally, to solve the nesting optimisation problem in network training, an equivalent model is constructed to remove the sub-optimisation and make gradient-based training optimisation feasible. A numerical study shows that the integrated structure leads to prediction results with a lower cost than those of an accuracy-based structure.(c) 2020 Published by Elsevier Ltd.
机译：光伏（PV）生成预测是用于将太阳能在动力系统和市场中集成的关键技术。准确性是大多数PV预测模型的目标，它代表了平均误差的最小化。然而，最小化预测误差是从预测不准确的影响获得最小成本。最低平均错误可能并不总是涉及最低成本。因此，本文提出了一种集成的光伏预测结构，其针对除了使用纯精度之外的预测误差的最小工业成本。机器学习模型的对象被修改为进一步的预测误差的工业成本，这是电网能量平衡的备用电力调度的备用生成参与成本。选择前馈神经网络作为集成的典型机器学习模型。另外，为了解决网络培训中的嵌套优化问题，构造了一种等效的模型以消除子优化，使基于梯度的训练优化可行。数值研究表明，集成结构导致预测结果与基于精度的结构的成本较低。（c）2020由elsevier有限公司发布

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