Predictive modelling of cooling consumption in nursing homes using artificial neural networks: Implications for energy efficiency and thermal comfort

The growing need for cooling within the built environment, propelled by climate change and the expansion of nursing homes due to the increase in life expectancy, highlights the urgency of implementing energy-efficient strategies in buildings occupied by older populations. As of today, there remains a need for comprehensive research into the influence of indoor and outdoor conditions, building, operational, and occupant characteristics, on energy consumption specifically for nursing homes. This study develops a systemic artificial neural network-based model with a multi-layer perceptron architecture to assess HVAC energy implications during the cooling season for older populations. Using monitored data from eight nursing homes, the model includes cooling area, construction age, outdoor and indoor temperatures, and outdoor relative humidity as inputs, and cooling consumption as the output. Results show excellent predictive capability (R 2 =0.95), with mean error of −0.5 kWh, root mean squared error of 13.7 kWh, mean absolute error of 10.2 kWh, and relative error of 0.051. These outcomes are better compared to linear models (R 2 ≈0.65) under the same data set. Adjusting operative temperatures adaptively can significantly enhance resident comfort and achieve up to 23.4 % energy savings, particularly in hotter, drier climates. These findings are of paramount importance for effective energy management in buildings. • Five-input neural network is trained to predict cooling consumption in nursing homes. • Neural network outperforms linear models under the same data set (R 2 =0.95 vs R 2 =0.65). • Generalised neural network usage extends beyond the nursing homes in the data set. • Nursing homes are over-cooled by 2–3ºC. Adjustments lead to energy savings of 23.4 %. • Drier and hotter climates generally offer more potential for energy savings.