A Machine Learning-Based Approach to Predict and Optimize the Performance of Zero Energy Building (ZEB): A Case Study for Florida
Abstract
Machine learning is currently one of the most searched fields aiming to solve real-life
problems. Building simulation software tools help engineers estimate building energy
behaviors before the actual construction, allowing implementation of more energy efficient
choices in building design and construction. Current building energy simulation software
tools are mostly physics-based and still lack the benefit obtained with machine learningbased modeling, which offers fast and less computationally expensive techniques to build
energy models and efficiently perform design optimization. This thesis presents a machine
learning-based approach for building energy modeling and optimizing design parameters to
minimize building’s energy consumption. The study is comprised of three main stages.
These include creating an EnergyPlus simulation model to generate a physics-based model
for the building with all building characteristics. The model is used to generate a database
containing input design parameters that are used in energy modeling and annual energy
consumptions for different energy models. The results obtained from this database are then
used in the second stage, which involves developing an artificial neural network-based
surrogate model. The neural network performs simulations by taking a set of inputs and
trying to predict an output. The inputs, in this case, are building design parameters and
control settings, while the output is the building energy consumption, photovoltaic system
power production, and the corresponding net site energy. The third stage is the optimization
stage implemented on the surrogate model to determine optimal design variables that provide
minimal energy consumption. Design parameter search space along with the surrogate model
are provided as inputs to the optimization algorithm. The study uses two different
optimization approaches, including the genetic algorithm and the Bayesian method. This
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study shows that the proposed machine learning-based strategy accurately estimates overall
energy usage and production. Furthermore, the model optimization is implemented on the
neural network at far less computational costs and time than the traditional strategies that
involve numerous co-simulation tools to obtain the same results. The developed approach
bridges between physics-based building energy models and strong optimization tools
available in python which can allow achieving global optimization.