Effectiveness of Occupant Behavioral Ventilation Strategies on Indoor Thermal Comfort in Hot Arid Climate

It is clear that the field of artificial intelligence (AI) as a decision-oriented tool has recently proven to be a viable alternative approach to solve environmental challenges. For example, artificial neural networks (ANNs) and support vector machines (SVMs), which are a subset of artificial intelligence, are going to be widely used to predict energy consumption in the buildings. The work aims to explore the use of user behaviour and smart and passive systems to improve energy efficiency and indoor environmental quality (IEQ) in buildings. The presence of users within buildings can affect process improvement. For example, users can contribute to energy efficiency by switching off artificial lighting during daylight hours. Furthermore, they can reduce the use of energy by changing their behaviour to act according to principles of sustainable development. In order to evaluate the impact of user behaviour on energy consumption, development of an assessment model based on AI can be useful. On the other hand, the use of a new concept from artificial intelligence in assessment tools can not only explore the potential benefits of approach but also provide ways to achieve an optimum level of efficiency