An Optimal and Learning-Based Demand Response and Home Energy Management System

This paper focuses on developing an interdisciplinary mechanism that combines machine learning, optimization, and data structure design to build a demand response and home energy management system that can meet the needs of real-life conditions. The loads of major home appliances are divided into three categories: 1) containing fixed loads; 2) regulate-able loads; and 3) deferrable loads, based on which a decoupled demand response mechanism is proposed for optimal energy management of the three categories of loads. A learning-based demand response strategy is developed for regulateable loads with a special focus on home heating, ventilation, and air conditioning (HVACs). This paper presents how a learning system should be designed to learn the energy consumption model of HVACs, how to integrate the learning mechanism with optimization techniques to generate optimal demand response policies, and how a data structure should be designed to store and capture current home appliance behaviors properly. This paper investigates how the integrative and learning-based home energy management system behaves in a demand response framework. Case studies are conducted through an integrative simulation approach that combines a home energy simulator and MATLAB together for demand response evaluation.