Smart-building forecasting energy using spatio-temporal CNN-LSTM approach: Case of smart hospital

Capturing complex spatio-temporal dependencies in energy data of connected infrastructures remains a significant challenge. Indeed, most of the proposed approaches focus on either spatial or temporal dependencies, without effectively modeling their joint influence. This paper presents a comprehensive spatio-temporal framework for energy forecasting in smart buildings, with structured representation of entities, events and relationships in the IoT ecosystem. For energy consumption prediction, we propose a hybrid CNN2D-LSTM model that combines convolutional layers to extract spatial features from multi-variable data with LSTM layers to capture temporal dependencies. The proposed model is instantiated on a smart hospital building which is characterized by continuous operation, high energy consumption and heterogeneous medical equipment. Under realistic constraints, a simulation-based dataset was generated and used for evaluation, reflecting dynamic behaviors in the event-driven system and realistic scenarios of energy consumption. The experimental evaluation demonstrates that the proposed model outperforms conventional approaches such as ANN, RNN, CNN, and LSTM and also hybrid models from the literature in terms of prediction accuracy. Comparisons were also conducted under different operating scenarios, including normal consumption, overconsumption, and under-consumption conditions, while maintaining a reasonable processing time. Beyond prediction, the proposed framework supports Green IoT principles by enabling energy management systems to monitor consumption, detect anomalies, and support proactive decision-making. It provides a scalable foundation for energy management in complex smart buildings.