Observed Data Energy Modeling Framework for Building Life Cycle Cost and Emissions Analysis

Supplementary Material

License: LGPL

Version: 1.0

Supplementary Building Energy Templates Include

Student Housing Building Classrooms with Labs Classrooms with Auditoriums Classrooms with Dancehalls

Code for Energy model and Optimization

The Python notebook file may be used to model different buildings similar to the (4) template files. Building size can be adjusted and a local weather file can be input to model different sized buildings in different locations. The Multi-Objective Optimization code can be used to define the Pareto Front using the results from the building energy model.

Code training cooling tower neural network

The Python notebook file may be used to training a cooling tower neural network using Adams or RMS optimizer. The code uses TensorFlow and the NN training data included in this repository. Training data was accessed through BAC selection software at: http://www.baltimoreaircoil.com/english/product-selection-software-public

Instructions (Google Colab):

1. Click on the link
2. Open the code in Google Colab
3. Run the code

Citation

To cite this work: Legorburu, G., & Smith, A. D. (2020). Incorporating observed data into early design energy models for life cycle cost and carbon emissions analysis of campus buildings. Energy and Buildings, 224, 110279. https://doi.org/10.1016/j.enbuild.2020.110279