This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-constrained microgrids. In contrast to conventional methods that prioritize economic optimization, the proposed framework incorporates state of health (SOH) aware control and adaptive closed-loop scheduling to enhance operational reliability and battery longevity. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for accurate SOH estimation, with lightweight Multi-Layer Perceptron (MLP) models supporting real-time scheduling and state of charge (SOC) regulation. Operational safety is maintained by keeping SOC within 20–80% and SOH above 70%. The proposed model training and validation are conducted using two real-world datasets: the Mendeley Lithium-Ion SOH Test Dataset and the DKA Solar System Dataset from Alice Springs, both sampled at 5-min intervals. Performance is evaluated across three operational scenarios, which are 2C charging with random discharge; random charging with 3C discharge; and fully random profiles, achieving up to 44% reduction in MAE and an R²; score of 0.9767. A one-month deployment demonstrates a 30% reduction in charging time and 40% lower operational costs, confirming the framework’s effectiveness and scalability for rural microgrid applications.

Heating, ventilation, and air conditioning (HVAC) systems account for 60% of the energy consumption in commercial buildings. Each year, millions of dollars are spent on electricity bills by commercial building operators. To address this energy consumption challenge, a predictive model named Bayesian optimisation Convolution Neural Network Multivariate Long Short-term Memory (BO CNN-M-LSTM) is introduced in this research. The proposed model is designed to perform load forecasting, optimizing energy usage in commercial buildings. The CNN block extracts local features, whereas the M-LSTM captures temporal dependencies. The hyperparameter fine tuning framework applied Bayesian optimization to enhance output prediction by modifying model properties with data characteristics. Moreover, to improve occupant well-being in commercial buildings, the thermal comfort adaptive model developed by de Dear and Brager was applied to ambient temperature in the preprocessing stage. As a result, across all four datasets, the BO CNN-M-LSTM consistently outperformed other models, achieving an 8% improvement in mean percentage absolute error (MAPE), 2% in normalized root mean square error (NRMSE), and 2% in R2 score.This indicates the consistent in performance of BO CNN-M-LSTM under varying environmental factors, highlight the model robustness and adaptability.Hence, the BO CNN-M-LSTM model is a highly effective predictive load forecasting tool for commercial building HVAC systems.