Quantifying the Impact of Customized Feedback on User Energy Consumption Behavior with Low-cost IoT Setup

In this paper, we present the results of an experimental study to understand and quantify the impact of personalized feedback on HVAC energy consumption and wastage inside buildings. We develop a scalable, low-cost Internet of Things (IoT) platform, which was deployed inside a campus building. The data collected was then used to estimate the HVAC energy consumption and wastage in the rooms using energy models and machine learning algorithms. We then design personalized feedback for users in our case study and determine the impact of feedback in changing the energy consumption behavior of users. Users are divided into two groups and each group receives different feedback. The results showed that the energy consumption reduced maximum by 24% and the wastage reduced from 84-100%, as effect of customized feedback.