End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning
This work addresses the challenge of demand response model identification for energy grid operators, offering a novel approach that could improve efficiency in managing electricity demand, though it appears incremental as it builds on existing deep learning and optimization techniques.
The paper tackles the problem of simultaneously identifying demand baselines and incentive-based agent demand response models from net demand measurements and incentive signals, proposing an end-to-end deep learning framework that integrates a differentiable optimization layer for user response and a neural network for baseline forecasting, with results showing accurate model identification even without prior baseline knowledge.
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.