基于深度学习的端到端需求响应模型识别和基线估计
End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning
摘要：
本文提出了一种新的端到端深度学习框架，该框架可以从净需求测量和激励信号中同时识别需求基线和基于激励的代理需求响应模型。该学习框架被模块化为两个模块：1）需求响应参与者的决策过程被表示为可微优化层，其将激励信号作为输入并预测用户的响应; 2）基线需求预测被表示为标准神经网络模型，其采用相关特征并预测用户的基线需求。这两个中间预测被整合，以形成净需求预测。然后，我们提出了一个梯度下降的方法，反向传播的净需求预测误差更新代理模型的权重和基线需求预测的权重，联合。我们证明了我们的方法的有效性，通过计算实验与合成的需求响应跟踪和大规模的现实世界的需求响应数据集。我们的研究结果表明，该方法准确地识别需求响应模型，即使没有任何先验知识的基线需求。
英文摘要：
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals. This learning framework is modularized as two modules: 1) the decision making process of a demand response participant is represented as a differentiable optimization layer, which takes the incentive signal as input and predicts user's response; 2) the baseline demand forecast is represented as a standard neural network model, which takes relevant features and predicts user's baseline demand. These two intermediate predictions are integrated, to form the net demand forecast. We then propose a gradient-descent approach that backpropagates the net demand forecast errors to update the weights of the agent model and the weights of baseline demand forecast, jointly. We demonstrate the effectiveness of our approach through computation experiments with synthetic demand response traces and a large-scale real world demand response dataset. Our results show that the approach accurately identifies the demand response model, even without any prior knowledge about the baseline demand.
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