Abstract
Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems. The model is based on using an optimized design of Gated recurrent unit (GRU) network and an enhanced metaheuristic algorithm, named Improved Alpine Skiing Optimizer (IASO). GRU is a recurrent neural network that has the ability to comprehend intricate temporal relationships within the input data. On the other hand, the IASO technique has been considered to be a population-based optimization technique emulating the downhill skiing behavior of skiers. The proposed GRU/IASO model is trained and tested utilizing data of real-world obtained through a commercial complex situated within an area characterized by high humidity and hot climate. By comparing the proposed method with some other commonly used techniques, including ---, the advantage of the suggested model regarding accuracy and robustness has been defined.