2019
DOI: 10.3390/fi11040088
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An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality

Abstract: With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize electric appliances’ operation schedules, this paper considers how to quantitatively evaluate a customer’s comfort satisfaction in energy-saving programs, and how to formulate the optimal energy-saving model based on this satisfaction evaluation. First, the paper … Show more

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Cited by 19 publications
(7 citation statements)
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References 26 publications
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“…Additionally, the implementation of edge computing along with IoT and cloud computing would be more efficient with bandwidth-intensive elements and latency-sensitive applications. Lin et al (2019) explored improving the energy efficiency of home energy management systems by evaluating customers' comfort satisfaction quantitatively and formulated an optimal energy-saving model. They classified the utility functions of electronic appliances as time sensitive and temperature sensitive and constructed a general utility function for determining energy-saving costs by incorporating the prospect theory from behavioural economics.…”
Section: Visionmentioning
confidence: 99%
“…Additionally, the implementation of edge computing along with IoT and cloud computing would be more efficient with bandwidth-intensive elements and latency-sensitive applications. Lin et al (2019) explored improving the energy efficiency of home energy management systems by evaluating customers' comfort satisfaction quantitatively and formulated an optimal energy-saving model. They classified the utility functions of electronic appliances as time sensitive and temperature sensitive and constructed a general utility function for determining energy-saving costs by incorporating the prospect theory from behavioural economics.…”
Section: Visionmentioning
confidence: 99%
“…Most researchers who model residential energy management systems use historical data 4 as the baseline against which to measure the performance of these devices. There is consensus in the field of demand response measurement and verification that the raw historical data of electricity usage may be insufficient to accurately describe what a customer might do in the future 5 .…”
Section: Introductionmentioning
confidence: 99%
“…In the literature, there are many studies conducted concerning the properties of domestic HEMS. For instance, Lin et al [15] developed an optimal energy-saving approach to minimize the energy cost in Guangdong, China. The lighting, air-condition, and some other common electrical equipment were considered in this study.…”
Section: Introductionmentioning
confidence: 99%