An information provision system to promote energy conservation and maintain indoor comfort in smart homes using sensed data by IoT sensors

Smart cities are often discussed in the context of promoting sustainability. A smart city operates using large numbers of networked sensors to collect, store, and analyze massive amounts of data in near-real time; these data may also have secondary applications. This study proposes one such use of the data gathered by home energy management systems (HEMS). Current HEMS focus mainly on energy efficiency, but residents typically value indoor comfort as well. HEMS collect various types of data that can be used to create an index for short-term indoor comfort called the predicted mean vote (PMV). This study implements a system to identify information within the stored data that is relevant to reducing electricity consumption while maintaining indoor comfort according to each resident's PMV preference. The system was tested in three households in Japan for 12 days in winter. The system reduced electricity consumption by 5.15% and increased the comfort satisfaction expressed as PMV by 42.3%. Qualitative assessments of indoor comfort increased by 16.4%. Providing users with information selected according to their PMV preferences was more effective in reducing electricity consumption and increasing indoor comfort than providing them with random information.