This release includes the complete dataset used in the paper "Optimizing Building Energy Systems Using Generalized Disjunctive Programming: Improving Model Precision and Decision-Making Efficiency." The dataset supports the analysis of building energy systems (BES) optimization, with a focus on minimum part-load constraints, discrete equipment sizing, pricing models, and subsidy policy integration.
Key contents of this release:
- Input data for all case studies conducted in the paper.
- Model scripts and configuration files used to implement Generalized Disjunctive Programming (GDP) and Mixed-Integer Linear Programming (MILP) comparisons.
- Detailed descriptions of each dataset and configuration settings for replicating the results.
This version marks the first public release of the data, ensuring full reproducibility of the study's findings.