Evaluating the impact of data preprocessing to develop a robust MEP-based forecasting model for building integrated with PCM
Nazir K. Memon S.A.
1 June 2025Elsevier Ltd
Energy
2025#324
Data quality is a crucial aspect to accurately predict the energy use of buildings utilizing machine learning methods. Data preprocessing can ensure data quality when a database does not match the criteria for evolving a robust prediction model. Regarding phase change material (PCM)-incorporated buildings, there was no study before this research evaluating the impact of data preprocessing for establishing a robust machine learning based model to forecast their energy consumption (EC). Therefore, for the first time, this research presents an application of the data preprocessing process to compare the results of the formulated multi-expression programming (MEP)-based prediction models accuracy for predicting the EC of PCM-integrated buildings using processed with actual databases. Data cleaning, outlier detection and removal, and data smoothing were performed on the actual EC database during the data preprocessing process. Results of model evaluation and validation processes for the articulated prediction models showed that the data preprocessing improved the MEP-based prediction model by 33 % to predict the EC precisely. Conclusively, model interpretability (sensitivity, parametric, and energy saving analysis) demonstrated that the developed more reliable prediction model provides energy savings of approximately 20 % by integrating optimum PCM.
Data preprocessing , Energy consumption , Model interpretability , Multi-expression programming , Phase change material
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Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
Department of Civil and Environmental Engineering
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026