An integrative study of home energy management for residential energy consumers

This paper studies how to optimize the energy usage of home appliances in the demand response framework from the consumer's perspective. The loads of major home appliances are divided into three categories: fixed loads, regulate-able loads, and deferrable loads. For efficient usage of the home appliances, an integrative optimization of the three category loads is needed. The paper investigates the relation of the integrative optimization and individual optimization of each category load. A regression-based learning strategy is employed to learn HAVC energy consumption model for development of more efficient DR policy. The study is conducted through an integrative computational experiment approach that combines a home energy simulator and MATLAB together for demand response development and evaluation. The paper examines how the integrative demand response of the residential home appliances are affected by dynamic pricing tariffs, seasons, and weather. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods