Development of surrogate models using artificial neural network for building shell energy labelling
| Autor: | D Daniel Cóstola, Ana Paula Melo, Jlm Jan Hensen, Roberto Lamberts |
|---|---|
| Přispěvatelé: | Building Performance |
| Rok vydání: | 2014 |
| Předmět: |
Engineering
Artificial neural network business.industry 020209 energy Building energy 02 engineering and technology Energy consumption 010501 environmental sciences Management Monitoring Policy and Law Machine learning computer.software_genre 01 natural sciences General Energy Surrogate model 0202 electrical engineering electronic engineering information engineering Facade SDG 7 - Affordable and Clean Energy Artificial intelligence business Building energy simulation computer SDG 7 – Betaalbare en schone energie Simulation 0105 earth and related environmental sciences |
| Zdroj: | Energy Policy, 69, 457-466. Elsevier |
| ISSN: | 0301-4215 |
| Popis: | Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of surrogate models for labelling purposes. An ANN was applied to model the building stock of a city in Brazil, based on the results of extensive simulations using the high-resolution building energy simulation program EnergyPlus. Sensitivity and uncertainty analyses were carried out to evaluate the behaviour of the ANN model, and the variations in the best and worst performance for several typologies were analysed in relation to variations in the input parameters and building characteristics. The results obtained indicate that an ANN can represent the interaction between input and output data for a vast and diverse building stock. Sensitivity analysis showed that no single input parameter can be identified as the main factor responsible for the building energy performance. The uncertainty associated with several parameters plays a major role in assessing building energy performance, together with the facade area and the shell-to-floor ratio. The results of this study may have a profound impact as ANNs could be applied in the future to define regulations in many countries, with positive effects on optimizing the energy consumption. |
| Databáze: | OpenAIRE |
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