Development of Low-Cost IoT Units for Thermal Comfort Measurement and AC Energy Consumption Prediction System

TL;DR

This paper presents a low-cost IoT system using Raspberry Pi for real-time thermal comfort monitoring and AC energy prediction, leveraging machine learning to promote energy-saving behaviors in office buildings.

Contribution

It introduces an affordable IoT-based system integrating AI and image recognition for thermal comfort assessment and energy prediction in small and medium offices.

Findings

Machine learning model achieved 97% R2 in energy prediction.

System effectively promotes energy-saving behaviors.

Real-time thermal comfort monitoring implemented successfully.

Abstract

In response to the substantial energy consumption in buildings, the Japanese government initiated the BI-Tech (Behavioral Insights X Technology) project in 2019, aimed at promoting voluntary energy-saving behaviors through the utilization of AI and IoT technologies. Our study aimed at small and medium-sized office buildings introduces a cost-effective IoT-based BI-Tech system, utilizing the Raspberry Pi 4B+ platform for real-time monitoring of indoor thermal conditions and air conditioner (AC) set-point temperature. Employing machine learning and image recognition, the system analyzes data to calculate the PMV index and predict energy consumption changes due to temperature adjustments. The integration of mobile and desktop applications conveys this information to users, encouraging energy-efficient behavior modifications. The machine learning model achieved with an R2 value of 97%, demonstrating the system's efficiency in promoting energy-saving habits among users.