Facilitating the implementation of neural network-based predictive control to optimize building heating operation
| Autor: | Marjan Savadkoohi, Marcel Macarulla, Miquel Casals |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Recursos Naturals i Medi Ambient, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Projectes i de la Construcció, Universitat Politècnica de Catalunya. GRIC - Grup de Recerca i Innovació de la Construcció |
| Rok vydání: | 2023 |
| Předmět: |
Edificis -- Estalvi d'energia
Historical data Mechanical Engineering Energies::Eficiència energètica [Àrees temàtiques de la UPC] Edificació::Instal·lacions i acondicionament d'edificis::Instal·lacions de climatització [Àrees temàtiques de la UPC] Building energy optimization Building and Construction Buildings -- Energy conservation Pollution Neural network Industrial and Manufacturing Engineering Neural networks (Computer science) Set-poin General Energy Xarxes neuronals (Informàtica) Predictive control Electrical and Electronic Engineering HVAC systems Civil and Structural Engineering |
| Zdroj: | Energy. 263:125703 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2022.125703 |
| Popis: | Simple neural network (NN) architecture is a reliable tool to transform reactive rule-based systems into predictive systems. Thermal comfort is of utmost importance in office buildings, which need the activation of heating systems at an optimal time. A high-performance NN predictive system requires a large training dataset. This can limit system efficiency due to the lack of enough historical data derived from thermal controllers. To address this issue, we generated, trained and tested a dataset of eight sizes using a calibrated building model. A set of key performance indicators (KPIs) was improved by studying the output performance. The effect of normalization and standardization preprocessing techniques on NN prediction ability was studied. Learning curves showed that a minimum of 1–4 months of data are required to obtain enough accuracy. Two heating seasons provide the optimal data size to calibrate the NN properly with high prediction accuracy. The results also revealed that building data from =two years slightly improve NN performance. The most accurate results in KPIs 90%) were obtained with preprocessed data. The effect of preprocessing on large training patterns was less than that of training patterns |
| Databáze: | OpenAIRE |
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