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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...
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...
This paper investigates how to develop a real-time demand response strategy from the consumer's perspective through a computational experiment approach. Typical conventional demand response methods are reviewed and their shortcomings are analyzed. Then, a smart demand response mechanism is proposed for potential real-time demand response implementation. An integrative computing platform that combines...
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