인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This study introduces a cooling control strategy focused on thermal comfort, aimed at reducing energy consumption while maintaining or improving indoor comfort in the extremely hot and dry climates of Saudi Arabia. Experiments were conducted in a full-scale climatic chamber using three operating modes: a constant-speed air conditioner set to 24°C (Case 1), an inverter air conditioner also set to 24°C (Case 2), and an intelligent controller (Case 3) that adjusts cooling based on real-time Predicted Mean Vote (PMV) values. In Case 3, an artificial neural network (ANN) model predicted the mean radiant temperature using easily measurable inputs—indoor air temperature, outdoor air temperature, air conditioner setpoint, and time-related variables. This prediction was then used to calculate PMV and dynamically adjust the cooling setpoint. Over a 24-hour test period, energy consumption was recorded at 16.1 kWh for Case 1, 8.7 kWh for Case 2, and 8.2 kWh for Case 3. This represents a 46% reduction in energy use with the inverter system compared to the constant-speed system, along with an additional 6% saving with the PMV-based control over the inverter baseline. In terms of thermal comfort, both Case 1 and Case 2 showed a cool-side bias, with mean PMV values of −0.9 and −0.7, and PMV unmet rates of 100% and 56%, respectively. In contrast, Case 3 achieved a mean PMV of 0.1 and a PMV unmet rate of only 6%, indicating that approximately 94% of occupied hours fell within the ASHRAE-55 comfort range. These results highlight that PMV-targeted, machine learning-enabled setpoint modulation can correct cool bias, enhance thermal comfort, and deliver additional energy savings beyond inverter-only operation in hot and dry climates.
본문·목차
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