실내 이산화탄소 농도 예측 기반 적응형 환기 제어 알고리즘 개발

Abstract

Purpose: This study aimed to develop an adaptive ventilation control algorithm for occupant-centric control (OCC). The control algorithm utilizes a real-time indoor carbon dioxide concentration prediction model that reflects occupant information and is continuously updated through daily learning. Method: The prediction model was developed using a long short-term memory (LSTM) learning algorithm based on data obtained from a living lab. The indoor CO2 concentration after 5 minutes was predicted through the data of the past 1 hour, and the prediction accuracy was evaluated with the test data. The adaptive ventilation control algorithm, which incorporates the prediction model, was then applied to the living lab for experiments to evaluate its real-time prediction accuracy, adaptability, and control performance. Result: As a result of the performance evaluation of the predictive model, the coefficient of variation of the root mean squared error (CVRMSE) was 1.78% and the R2 was 0.97. The adaptability evaluation over four days presented an improvement in CVRMSE from 1.78% to 1.13%, which is approximately 36.52% improvement from the initial performance. During the experiment with the adaptive ventilation control algorithm, the accuracy decreased slightly with a CVRMSE of 2.90% and R2 of 0.98, likely due to frequent ventilation control leading to large data variations. Despite short period of the indoor carbon dioxide concentration exceeding 1,000 ppm, the control was effective. According to the results, it is expected that providing comfortable indoor air quality at all times can be achieved by improving the optimal control cycle and supplementing data learning for various control modes in future research.