tstran155 / Optimization-of-building-energy-consumption

This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.

Date Created 2023-02-18 (2 years ago)

Commits 66 (last one 2 years ago)

Stargazers 24 (-1 this week)

Watchers 1 (0 this week)

Forks 6

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RepositoryStats indexes 628,089 repositories, of these tstran155/Optimization-of-building-energy-consumption is ranked #625,213 (0th percentile) for total stargazers, and #554,057 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #18,783/18,867.

tstran155/Optimization-of-building-energy-consumption is also tagged with popular topics, for these it's ranked: machine-learning-algorithms (#292/293)

All Topics

hvac ipopt pymoo pyomo nsga-ii deterministic gurobi-solver sobol-indices building-energy cross-validation xgboost-regression bayesian-optimization nonlinear-optimization multivariate-regression probabilistic-inference stochastic-optimization latin-hypercube-sampling machine-learning-algorithms multi-objective-optimization genetic-optimization-algorithm

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66 commits on the default branch (main) since jan '22

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The only known language in this repository is Jupyter Notebook

updated: 2025-03-15 @ 04:34pm, id: 603548641 / R_kgDOI_lr4Q