DISCIPLINA (SHITSUKE)

This research aims to optimize energy performance of whole building renovation considering LCC and LCA. The particular focus of this research was placed on:

Module 1: Developing SBMO model of institutional building renovation considering TEC, LCC

and LCA. The proposed model initially develops a framework for data collection and preparation to define the renovation strategies and proposes a comprehensive database including different renovation methods. Using this database, different renovation scenarios can be compared to find the near-optimal scenario based on the renovation strategy. Each scenario is created from the combination of several methods within the applicable strategy. The methods include the factors related to the building envelope, HVAC, and lighting system. The SBMO model simulates the process of renovating buildings by using the renovation data in energy analysis software to analyze TEC, LCC, and LCA and identifies the near-optimal renovation scenarios based on the selected renovation methods. Furthermore, an LCA tool is used to evaluate the environmental sustainability of the final decision.

A case study of one floor of an existing building was studied to assess the implementation of the developed model. LCA and TEC have strong linear correlation in comparison with the LCC and TEC. It is worthy to mention that the optimization in the first case has a larger number of Pareto solutions because energy consumption and LCC are conflicting objectives. Comparing the ratio of LCC per TEC for the Pareto solutions clarifies their efficiency. This comparison demonstrates that there is a better potential in reducing TEC in Scenario B than in Scenario A since with a slight increase in LCC, significant decrease in TEC is attained. Furthermore, the energy saving improvement from scenario A to B is 24,325 kWh/year, which is significant.

Module 2: Developing surrogate ANN for selecting near-optimal building energy renovation

methods considering energy consumption, LCC, and LCA. The proposed model can be used to predict TEC, LCC and LCA of the potential renovation scenarios of existing institutional buildings. The proposed model couples the optimization power of SBMO with modeling capability of ANNs. In the first phase, the optimization process, coupled with the SBMO, forecasts the building TEC, LCC, and LCA pairwise. Then, two different ANNs were developed to predict and

model TEC, LCC, and LCA of renovating combinations of elements of an existing institutional building (i.e., R, EW, W, FT, WWR, HVAC, COS, HOS, Li, and EWO). To do so, initially five- layer ANNs were defined with ten neurons in the input layer, three neurons in the hidden layers, and two neurons in the output layer. Then a cross-validation method was used to reach the optimal values. It was found that in this model, the higher number of layers and neurons significantly improves the accuracy of the ANN. Finally, a five-layer ANN was defined with 10-5-6-4-2 neurons in input, hidden (three layers), and output layers.

The case study was implemented based on the results of the SBMO. Different ANNs are generated in MATLAB® by using the outcomes of DesignBuilder energy simulations for network training and testing. The regressions between the ANN predictions and target SBMO outputs plots show an acceptable agreement between the predictions and the SBMO, with regression coefficients close to 1.

Module 3: Developing a generative deep MLM for whole building renovation scenarios using

semi-supervised VAE. The model can generate different renovation scenarios for building envelope, HVAC, and lighting system considering TEC and LCC. First, unsupervised VAE-0 has been exploited as a basic model prior on the developments of the final models. Then, three different semi-supervised VAE architectures have been developed that can learn from a labeled dataset with very fast inference processes.

Two VAEs were defined for each architecture. The VAE models, which generate the best training validation (i.e., MSE) are maintained. Initially a five-layer VAE was defined with 20 neurons in the encoder input layer, three hidden layers, and 20 neurons in the decoder output layer. Then a three-layer VAE was defined with 20 neurons in the encoder input layer, only one hidden layer, and 20 neurons in the decoder output layer. It was found that the VAEs with three hidden layers have best performance. Generally, increasing the number of samples in a dataset improved the accuracy.

Different configurations of VAEs 1, 2 and 3 have been studied. Convergence for the training is achieved if MSE is stabilized over certain iterations or if the maximum number of epochs is reached. Each network was trained, tested, and validated using different samples and the best combination was selected for each architecture considering MSE. An increase in the amount of

validation error is the indicator of overfitting. In this case, the backpropagation should be stopped. The training steps in VAEs are repeated many times for each architecture and the result with least validation error is reported in Table 6-2.

For validation of results, a comparison between the results of DesignBuilder as BEM and the output of the trained VAEs has been done, and an overall good agreement has been observed. The result shows that the networks have not committed underfitting.

The results show that generative VAEs 1, 2, and 3 can learn approximations of input features and deploy as generative models. The results showed some interesting behaviors of the proposed models. Firstly, the approximation accuracy of different VAEs is high. This is due to the generalization capability of the VAE. Secondly, overfitting should be considered if the loss function remains steady for a period of time or if the loss function has a value very close to zero. Finally, if the input parameters have higher levels of difference, the model has better capability for prediction. Using more parameters for training and testing was beneficial to avoid the loss of information problem. Furthermore, the computational time saving associated with the proposed VAEs is significant, and it is fair to say that the proposed model is feasible. The proposed VAEs can provide results in less than 1 second.

Compared with traditional ANNs (Module 2), VAEs can be used for different proposes (i.e., dimensionality reduction, feature extraction, and feature generation) and adjust numbers of neurons and layers to fit for different labeled datasets. Furthermore, learning from large-scale labeled datasets based on DNN is efficient and suitable for generalization.