Optimal design configuration and operating sequencing of hybrid chiller plant: A case study of hotel building in Cairo, Egypt

Deep clustering of cooperative multi-agent reinforcement learning to optimize multi chiller HVAC systems for smart buildings energy management

“…Another challenge with MFIS for ensuring multiple ambiguous crisp outputs is the associated parameter tuning during the training process [25]. The challenges for conventional OCSC are to provide optimal load distribution over all chillers and set an inappropriate number of operating chillers [26]. In recent years reinforcement learning (RL) has emerged as one of the most techniques to deal with such challenges and optimization methods to reduce the energy consumption of the plant chiller.…”