Optimization of Multi-Layer Neural Network-Based Cooling Load Prediction for Office Buildings Through Data Preprocessing and Algorithm Variations
Year: 2026
Type: article
Abstract: Accurate forecasting of cooling loads is essential for the effective operation of Building Energy Management Systems (BEMSs) and the reduction of building-sector carbon emissions. Although Artificial Neural Networks (ANNs), particularly Multi-Layer Perceptrons (MLPs), have shown strong capability in modeling nonlinear thermal dynamics, their reliability in practice is often limited by inappropriate training algorithm selection and poor data quality, including missing values and numerical distortions. To address these limitations, this study conducts a comprehensive empirical investigation into the effects of training algorithms and systematic data preprocessing strategies on cooling load prediction performance using an MLP model. Through benchmarking ten distinct training algorithms under identical conditions, the Levenberg–Marquardt (LM) algorithm was identified as achieving the lowest prediction error when integrated data preprocessing was applied. In particular, the application of data preprocessing reduced the CvRMSE from 18.56% to 6.03% during the testing period. Furthermore, the proposed framework effectively mitigated zero-load prediction errors during non-cooling periods and improved prediction accuracy under high-load operating conditions. These results provide practical and quantitative guidance for developing robust data-driven forecasting models applicable to real-time building energy optimization.
Source: Buildings
Authors: Namchul Seong, Daeung Danny Kim, Goopyo Hong
Institutions: Cheongju University, Kangwon National University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G1127354080, G3812690899, G5441095707
OpenAlex ID: W7126040810