Abstract

Buildings account for a significant share of final energy consumption, with space heating representing one of the major energy uses in residential buildings. Therefore, improving the thermal performance of building envelopes is an important strategy for reducing energy demand. Green roofs can contribute to this objective by modifying roof thermal properties and reducing heat losses through the building envelope. This study investigates the use of machine learning to predict annual heating demand and potential heating energy savings associated with replacing conventional roof configurations with a selected green roof assembly in a representative stock of Macedonian buildings. A representative dataset comprising 2934 building cases based on post-2013 buildings designed in accordance with the national energy-performance regulations was assembled. The dataset covers a wide range of building typologies, envelope thermal properties, climatic conditions and heating schedules. Three supervised learning models, Random Forest, Artificial Neural Network and Extreme Gradient Boosting (XGBoost), were developed and compared. The results show that XGBoost achieved the highest predictive accuracy and the best computational efficiency, with test coefficients of determination of 0.9901 for the heating demand of conventional roof buildings and 0.9956 for green-roof-related heating energy savings. Most simulated buildings showed heating energy savings of up to 10% following green roof implementation, while only a limited number of cases exhibited increases in heating demand of up to 3%. The feature importance analysis identified heated floor area, heating duration and wall area as the major drivers of heating demand in conventional roof buildings, whereas roof thermal transmittance was the most influential factor governing green-roof-related heating energy savings. The findings demonstrate that machine learning can reliably reproduce the results of the established energy performance assessment methodology and provide rapid estimates of the potential heating energy savings associated with replacing conventional roofs with a selected green roof system across a representative building stock. The proposed approach can support engineers, urban planners and architects in the early-stage assessment of green roofs as an energy-efficient measure.

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