A Lightweight Multi-Layer Perceptron Approach for Carbon Emission Prediction of Public Buildings Under Low-Dimensional Data Scenarios

Amid global efforts toward carbon neutrality, carbon emission accounting in the construction sector has become essential for sustainable design. Public buildings, with complex energy systems and high operational loads, are major carbon emitters. However, early design stages often provide only low-dimensional parameters—such as floor area, number of floors, and location—limiting conventional regression methods. This study develops a lightweight prediction framework using a multilayer perceptron (MLP) neural network. Feature engineering constructs composite indicators—layers per unit area (LPA) and height-to-area ratio (HAR)—to quantify spatial compactness and vertical density. A three-layer MLP with Swish activation, adaptive L2 regularization, and Dropout reduces overfitting and improves generalization. Tests show the model achieves a mean absolute error of 4160 tCO2 and R2 of 0.966, reducing prediction error by 54.7% compared to linear regression. For high-rise buildings (>15 floors), error remains below 8.1%. SHAP analysis highlights floor area as the dominant factor (51.2%), while HAR and LPA jointly improve accuracy by 5.8%. A Python-based tool is developed for rapid emission estimation during design. Using 150 samples and 10-fold cross-validation, this work demonstrates the potential of deep learning in low-dimensional carbon prediction, offering a practical reference for early-stage green building design, though generalizability requires further validation with larger datasets.