Accurate building performance forecasting is critical for the design and renovation of energy-saving structures, but existing methods face four key challenges: heterogeneous data fusion (sensor streams, design parameters, and environmental sequences), non-stationary physical time series, model interpretability, and sample efficiency (e.g., limited commissioning data). To address these challenges, this paper proposes Fusion-Grounded Forecasting (FGF), which is a framework integrating a gated adaptive fusion layer, deterministic trend-season decomposition, an additive predictor with component decomposition, and Bayesian regularization. This framework is designed for next-hour forecasting broadcast to hourly resolution using hourly sensor data and monthly design parameters. The dataset covers 36 months (approximately 25,920 h). In addition to the combination of existing modules, the novelty lies in the integrated architecture, in which interpretable constraints can adjust the fusion layer in both directions, with decomposition prediction alignment supporting component attributes. The framework is verified on a proprietary 36-month dataset from institutional buildings using standard prediction metrics (MAE, RMSE, MAPE, and directional accuracy) and ablation studies for comparison against 10 baselines: SARIMAX, GPR, LSTM, XGBoost, N-HiTS, Informer, Autoformer, NAM, a physics-informed hybrid, and TFT. FGF achieves a 3.1% MAPE and 92.5% directional accuracy in hourly cooling load forecasting. Ablation confirmed the contribution of each module: removing gated fusion increased the MAPE to 6.8%. Compared with manual feature engineering, the speed of the framework is increased by 1680 times, and the cost is reduced by 99.6%. The explanatory index (counterfactual reliability: 0.95; Stability of functional importance: 0.11) is in compliance with audit requirements. These results indicate that FGF connects descriptive physics with quantitative prediction. However, this study is limited to a single institutional building; transferability to residential, commercial, or industrial buildings requires further verification. While waiting for this verification, FGF has demonstrated its potential as a transparent and efficient tool to build performance models.
Chen et al. (Wed,) studied this question.