{'fraction_non_alphanumeric': 0.01556420233463035, 'fraction_numerical': 0.0, 'mean_word_length': 5.274390243902439, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Dynamic zone modelling for HVAC system control

This paper presents the development and validation of a dynamic zone model used for improved control of a heating, ventilation and air conditioning (HVAC) system to reduce energy consumption and improve the quality of the indoor environment. In particular, the paper focuses on a zone modelling technique that uses physical-principles based real-time model fitting and prediction methodology, taking advantage of genetic algorithm based problem solving. An air-conditioning zone model is deduced from an energy and mass balance and then expressed in terms of electric circuit theory, where the electric circuit is used to represent functions of the building elements. Experimental results for real-time zone model fitting and prediction are given. The results show that our model is capable of accurately predicting the indoor temperature of a dynamic zone. This dynamic model is useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems.