Cooling Load in The Under-Actuated HVAC Zone

Description

Cooling load in an HVAC system refers to the total heat energy that must be removed from an indoor space to maintain a desired temperature and thermal comfort level. This load is influenced by various internal and external factors, including occupant activity, electronic device usage, solar radiation, ventilation rates, and environmental conditions. It comprises both sensible heat, associated with temperature changes, and latent heat, related to moisture removal. Effective cooling load estimation is essential for optimizing HVAC system performance, ensuring energy efficiency, and enhancing occupant well-being. An under-actuated zone within an HVAC system denotes a spatial domain where cooling mechanisms are not independently controlled or fully modulated in response to real-time thermal variations and occupancy patterns. These zones exhibit constrained adaptability due to shared ventilation networks, limited sensor deployment, or centralized control strategies, resulting in suboptimal climate regulation. Consequently, fluctuations in cooling demand driven by dynamic occupant behaviors and environmental perturbations may lead to thermal discomfort, inefficient energy utilization, and a mismatch between conditioned air supply and actual thermal requirements. Cooling load measurement in under-actuated zones presents a significant challenge due to the complex interplay between fluctuating occupant behaviors, environmental conditions, and the limitations of conventional HVAC control strategies. Accurate measurement requires continuous monitoring of thermal dynamics within spatially constrained zones where airflow distribution is not uniformly regulated. In this repository, cooling load measurement was conducted in an under-actuated zone within the Universitas Trilogi Library, where one of the rooms exhibits under-actuated characteristics and is further divided into three distinct zones. Data collection was systematically performed over a 14-week period from September to December 2023, during the active academic semester. This extensive measurement campaign aimed to capture the nuanced interactions between occupancy, electronic device usage, and environmental variations, providing valuable insights into the cooling demand of under-actuated zones and facilitating the development of an optimized predictive model for energy-efficient HVAC management. During this period, occupant behavior was systematically recorded at five-minute intervals across all three monitored areas to ensure a granular understanding of real-time fluctuations in activity patterns. This extensive measurement campaign aimed to capture the nuanced interactions between occupancy, electronic device usage, and environmental variations, providing valuable insights into the cooling demand of under-actuated zones and facilitating the development of an optimized predictive model for energy-efficient HVAC management.

Steps to reproduce

The data for this study was gathered over a series of academic sessions spanning one full semester, aligning with the regular academic schedule on campus. This approach was adopted to ensure that data collection captured a representative range of campus activities, as these occur consistently throughout the 14-week semester. Data collection was conducted using both manual and automated methods to enhance accuracy and reliability. Manual techniques involved direct observation, where the number of occupants, their activities, and electronic device usage were systematically recorded through CCTV footage analysis. Automated methods incorporated sensor-based monitoring systems, which continuously collected data on environmental conditions and occupant behavior. The integration of these two approaches ensured a comprehensive dataset that accurately reflected dynamic variations in occupancy and environmental factors. The collected occupant behavior data was subsequently processed to compute cooling load estimations, applying established formulas to derive key metrics. This dataset, structured as the occupant dataset, consists of occupant number load, activity load, and electronic usage load. Beyond its immediate application in cooling load measurement, the dataset offers opportunities for further analysis and refinement. Researchers can re-engineer the dataset by examining time intervals, seasonal variations, periodicity patterns, and zonal distinctions to uncover deeper insights into occupancy-driven cooling demands. Additionally, feature engineering techniques can be applied to generate new variables, capturing complex interactions between occupant behavior, environmental conditions, and HVAC performance. These enhancements enable the development of more sophisticated predictive models, ultimately improving energy-efficient cooling load management in under-actuated zones.

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