AIM
- To estimate building heating and cooling loads accurately with respect to different building envelope design and climate conditions.
- To replace the energy analysis tool with trained ANN model or machine learning solutions for faster and accurate estimation of single design or optimization of the building envelope.
CURRENT STATE OF THE RESEARCH
- A neural network with alternative architectures and activation functions is constructed and the weights of the network is updated with stochastic gradient descent algorithms.
- The performance of the proposed network is tested with 8 building envelope inputs and 2 energy load outputs. The results of the study* shows that the proposed ANN improves the accuracy of estimation significantly compared to literature studies.
- In the ongoing research, the relations between both envelope design and climate inputs with energy loads output have being examined to construct a more comprehensive and accurate model for replacing analysis tool in the design stage.