An inverse method for calculation of thermal inertia and heat gain in air conditioning and refrigeration systems

Gray-box model for energy-efficient selection of set point hysteresis in heating, ventilation, air conditioning, and refrigeration controllers

“…When the compressor is on, its amount of power consumption depends on several factors such as air temperature, ambient temperature, and refrigerant pressure. However, within the narrow range of the set points, the compressor power can be assumed constant [25]. Therefore, the average compressor energy consumption per unit time is directly proportional to the amount of time that it is on.…”

“…Every two consecutive processes are called a 'temperature swing'. Useful information can be extracted from the study of temperature swings in any HVAC-R application [25].…”

A Self-Adjusting Method for Real-Time Calculation of Thermal Loads in HVAC-R Applications

“…Khayyam et al [7,8,17,37] Automotive Fuzzy predictive control Barnaby et al [14] Residential building HBM Fayazbakhsh and Bahrami [15] Automotive HBM Arici et al [16] Automotive Analytical energy balance Li et al [18] Office building Support vector machine and artificial neural network Kashiwagi and Tobi [19] Residential building Artificial neural network Ben-Nakhi and Mahmoud [20] Office building General regression neural network Yao et al [22] Office building Analytic hierarchy process Solmaz et al [23] Automotive Artificial neural network Sousa et al [21] Generic Fuzzy predictive control Fayazbakhsh et al [24] Freezer room HBM Wang and Xu [25,26] Office building Genetic algorithm Zhai et al [27][28][29][30][31][32] Building CFD general. As such, the same concept can be used for residential buildings, office buildings, freezer rooms, and vehicle air conditioning systems.…”

“…Solmaz et al [23] used the same concept of neural networks to predict the hourly cooling load for vehicle cabins. Fayazbakhsh et al [24] proposed a simple method that can estimate the total heat gain and thermal inertia of the room using an inverse calculation method and real-time temperature measurements.…”

Real-time thermal load calculation by automatic estimation of convection coefficients

“…Several studies (Li et al, Jan. 2009;Kashiwagi and Tobi, 1993;BenNakhi and Mahmoud, 2004;Sousa et al, 1997;Yao et al, 2004;Solmaz et al, 2014;Fayazbakhsh et al, 2015;Liang and Du, 2005) show that artificial intelligence algorithms such as neural networks, genetic algorithm, and fuzzy logic can help estimate the thermal loads in HVAC-R systems. Such models focus on relating the thermal load to parameters such as the ambient temperature by learning from real-time measurements rather than explicitly using the heat transfer equations.…”