# Learning Dynamical Demand Response Model in Real-Time Pricing Program

## TL;DR

This paper introduces a dynamical demand response model that captures temporal customer behavior in real-time pricing, significantly improving prediction accuracy over traditional static models.

## Contribution

It proposes a novel dynamical DR modeling framework using linear, neural network, and RNN-based approaches, learned from historical data.

## Key findings

- Dynamical models outperform static models in prediction accuracy.
- Recurrent neural networks effectively capture temporal EUC behaviors.
- The proposed models adapt to different state representations.

## Abstract

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.