The use of daylight in buildings to save energy while providing satisfactory environmental comfort has increased. Integration of the daylighting and thermal energy systems is necessary for environmental comfort and energy efficiency. In this study, an integrated meta-model for a daylighting, heating, ventilating, and air conditioning (IDHVAC) system was developed to predict building energy performance by artificial lighting regression models and artificial neural network (ANN) models, with a database that was generated using the EnergyPlus model. The design of experiments (DOE) method was applied to generate the database that was used to train robust ANN models without overfitting problems. The IDHVAC system was optimized using the integrated meta-model and genetic algorithm (GA), to minimize total energy consumption while satisfying both thermal and visual comfort for occupants. During three months in the winter, the GA-optimized IDHVAC model showed, on average, 13.7% energy savings against the conventional model.
- Artificial neural network (ANN)
- Design of experiments (DOE)
- Energy efficiency
- Genetic algorithm (GA)
- Integrated energy system modelling
ASJC Scopus subject areas
- Civil and Structural Engineering