Information provision system in a home energy and comfort management system for energy conservation

Abstract

A “smart City” includes many technological components (e.g., smarter intelligent transporting system, energy sharing, and safety and security service) that use the Internet communication technology, The Internet of Things (IoT) has an especially important role. The energy management system (EMS) is one of the main components in a smart city and is intended to conserve energy by using IoT technology. EMS has two typical parts, the building energy management system (BEMS) and home energy management system (HEMS). Both should be widely installed in buildings and the households to achieve energy conservation. This paper focuses on HEMS and presents a home energy and comfort management system that utilizes predicted mean vote (PMV) as an estimator of personal comfort by using data collected by IoT. HEMS should be implemented widely in order to effectively reduce electricity consumption; however these systems only consider a reduction in consumption. In order to create a sustainable HEMS, comfort should also be considered, because energy conservation alone is not enough to change a user's motivation for installing the system. Therefore, our system includes two processes, one is to collect environmental and physical data by using networked sensors and the other is to submit web-based questionnaires for calculating PMV values. PMV is one of the indicators of personal comfort in buildings, including homes. The other part of this system selects information from the collected data to enhance changes in personal behavior every 30 min to provide the preferred level of comfort. The information is created for each person's comfort range because different individuals have different comfort preferences. The system focuses on the information provision function of HEMS, and both energy conservation and comfort in houses at 15-min intervals. In order to test the applicability of the system, the system was installed in two test households for two weeks. In the first week, environmental and physical data were collected. This created a benchmark of electricity consumption. In the second week, information was provided. This was used to customize the comfort level for each person. In order to evaluate the system, the amount of electricity consumption reduced in the first week, and levels of comfort in the house were compared with the PMV values. As a result, the evaluation showed that the system contributed to an approximate 4.1% reduction of electricity consumption, and approximately 45.7% increase in personal comfort level.