ScholarWorks@성균관대학교

초록

This study proposes an AI-based wireless network temperature measurement system designed to capture and monitor fine-grained temperature variations within a grid-partitioned indoor environment. Unlike conventional thermostats that provide only a few point measurements, the proposed system employs a dense array of wireless sensor nodes distributed across a spatial grid to achieve high-resolution spatial coverage. Sensor readings are transmitted in real time to a centralized data aggregation server, which performs data cleaning, anomaly filtering, and integration with historical records. The core of the approach is an LSTMbased prediction model capable of short-horizon forecasting of temperature dynamics in each grid cell by leveraging temporal dependencies across multi-sensor data streams. By combining dense sensing, robust preprocessing, and predictive analytics, the system enhances spatial resolution and improves both the accuracy and responsiveness of indoor climate control. Early detection of anomalies such as localized hotspots or cooling inefficiencies allows proactive HVAC adjustments that reduce energy consumption, mitigate thermal discomfort, and prevent operational faults. Field experiments demonstrate that the proposed framework delivers real-time forecasting with low latency and high detection sensitivity, supporting adaptive decision-making in dynamic indoor environments. This integrated sensing-and-prediction paradigm represents a scalable and energy-aware solution for next-generation smart buildings, contributing to improved occupant comfort, sustainable energy management, and overall operational efficiency.

키워드