Sustainable artificial intelligence-based heating and cooling systems in load shedding events among commercial malls in Ghana

Purpose Load shedding and grid instability continue to pose a significant challenge for commercial buildings in West Africa. Various artificial intelligence–Heating, Ventilation and Air Conditioning (AI–HVAC) system algorithms show promise in improving energy efficiency across different models. However, its performance under load-shedding conditions in shopping malls remains understudied. This study assesses the resilience of AI-controlled systems under load-shedding conditions in shopping malls in Ghana. Design/methodology/approach A mixed quasi-experimental design was employed, integrating sensor data with simulation models and conducting interviews with HVAC professionals in Ghana’s shopping malls. AI-based control strategies, such as Model Predictive Control (MPC), Data-enabled Predictive Control (DeePC) and Reinforcement Learning (RL), were tested across the malls to assess their response times, thermal comfort and energy consumption. Findings The study found that RL achieved the lowest response latency, the highest energy consumption, the highest thermal comfort and the highest uptime (95.4%) compared with MPC and DeePC. Furthermore, DeePC established moderate performance across the indicators, while MPC reported the lowest values. Qualitative results indicate limited awareness of AI-based systems but a strong willingness to adopt them, given their expected benefits. Practical implications The RL–HVAC systems offer a viable solution for enhancing energy resilience during load shedding in shopping malls. Although beneficial, this requires shopping management readiness and professional training. Originality/value This critical performance of RL, along with other AI-based algorithms, in AI-controlled HVAC systems under unstable power in urban areas represents a new direction compared with MPC and DeePC functionalities in energy simulations in cold-climate regions.