Enhancing Indoor Cooling Prediction Using Empirical Models Incorporating Occupancy and Humidity | Chapter 10 | New Horizons of Science, Technology and Culture Vol. 4

 

Indoor air temperature is one of the key factors for maintaining the indoor air quality, energy consumption and optimum moisture. Accurate prediction of indoor temperature is crucial for optimising energy use and ensuring thermal comfort in air-conditioned environments. The study presents an empirical approach to model the cooling behaviour of a controlled room under varying conditions of air conditioner (AC) setpoint, occupancy, and humidity. While previous studies have often focused on simplified linear or Newtonian cooling models, most have neglected the combined effects of humidity, occupancy, and AC setpoint on cooling dynamics, resulting in limited real-world applicability. Three predictive models, linear, exponential (Newtonian cooling), and empirical, were developed from the experimental data collected for the time taken for every 0.5°. A drop in room temperature. Each model attempts to estimate the cooling rate and predict room temperature at different time intervals, which allows us to determine and compare Newton's coefficient of cooling under each condition. The experimental design involved controlled cooling sessions using a standard air conditioner with setpoints fixed at 15 °C, 20 °C, and 25 °C in separate trials. A quantitative comparison of the three models under each setpoint and occupancy condition was done using statistical analysis. The empirical model, which incorporates humidity, occupancy, and room volume, demonstrated superior accuracy over traditional linear and exponential models, as evidenced by lower mean error and root mean square error (RMSE), and a higher coefficient of determination (R²). The empirical model showed excellent agreement with actual observations, with a mean percentage deviation of just 10.3%, a root mean square error (RMSE) of 0.15 deg. C, and a high R² value of 0.97. It successfully predicted the cooling time within – 20 to +20 seconds and accurately captured the cooling coefficient trends with respect to temperature setpoint and occupancy. The study establishes a reliable framework for predictive climate control based on real-world thermal interactions. By accurately predicting the room cooling behaviour under varying human occupancy and humidity levels, the system can make informed decisions for optimised air conditioner operation, thereby enhancing energy efficiency.

 

Author(s) Details

 

Ashish Madhukar Jadhav
ICAR-IIWM, Bhubaneswar, India.

 

Omkar Jadhav
MSBTE, India.

 

Poonam Ranpise
Baburaoji Gholap College, Pune, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v4/6177