In day-ahead market (DAM), load serving entities (LSEs) are required to submit their future load schedule to market operator. Due to the cost computation, we have found the inconformity between load accuracy and cost of power purchase. It means that more accurate load forecasting model may not lead to a lower cost for LSEs. Accuracy pursuing load forecast model may not target a solution with optimal benefit. Facing this issue, this paper initiates a beneficial correlated regularization (BCR) for neural network (NN) load prediction. The training target of NN contains both accuracy section and power cost section. Also, this paper establishes a virtual neuron and a modified Levenberg-Marquardt algorithm for network training. A numerical study with practical data is presented and the result shows that NN with BCR can reduce power cost with acceptable accuracy level.

中文翻译：

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具有有益相关正则化的神经网络电力市场负荷预测


在日前市场（DAM）中，负荷服务实体（LSE）需要向市场运营商提交未来的负荷计划。通过成本计算，我们发现负荷精度与购电成本不相符。这意味着更准确的负荷预测模型可能不会为伦敦SE带来更低的成本。追求准确度的负荷预测模型可能无法获得最佳效益的解决方案。面对这个问题，本文提出了一种有益的相关正则化（BCR）用于神经网络（NN）负载预测。神经网络的训练目标包含精度部分和功率成本部分。此外，本文还建立了虚拟神经元和改进的 Levenberg-Marquardt 算法用于网络训练。提出了使用实际数据的数值研究，结果表明带有 BCR 的神经网络可以以可接受的精度水平降低电力成本。