Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs.

向各个居住者提供个性化的能源使用信息，可以在商业建筑中采用节能意识的行为。但是，由于难以收集个性化数据，跟踪个人行为以及将个性化定制信息传递给个人，因此实施个性化反馈仍然面临挑战。如今，由于这些技术可以融合不同的数据流，因此它们在各种应用中都使用了物联网（IoT）技术，包括实时监控，控制和决策。在本文中，我们提出了一种新颖的基于IoT的智能手机能源助手（iSEA）框架，该框架可促进商业建筑中的能源感知行为。iSEA通过跟踪他们的智能手机来跟踪个人，使用深度学习方法确定其能源使用情况，并提供个性化的量身定制反馈以影响其使用情况。iSEA特别使用能源利用效率指数（EEI）来了解行为并将其分类为有效和无效行为。iSEA体系结构包括四层：物理层，云层，服务层和通信层。在一个有十名乘客的商业建筑中，在十二周的时间内实施iSEA的结果表明，这种方法在增强个性化能源使用行为方面是有效的。在实验期结束时，通过跟踪乘员的EEI测算出平均节能34％。此外，结果表明，商业建筑居住者在离开时常常忽略对照明系统的控制，这导致在非工作时间浪费能源。通过利用商业建筑中现有的IoT设备，iSEA做出了巨大贡献，以最小的成本支持了研究工作，以感测和增强能源感知行为。