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In 2019, when electrical system energy losses are included, about 39% of the total U.S. end-use energy was consumed by the residential and commercial buildings according to EIA. Thus, advanced and optimal operations in building energy systems will reduce their energy uses and improve indoor environmental conditions. To achieve optimal performance in the building systems, accurate components models became crucial to predict the system’s performance and finally optimize it. This paper proposes an integrated two-level optimization approach to optimize the performance of the chilled water VAV system and a packaged DX VAV system. The first level of optimization is a Component Model Optimization (CMO) using genetic algorithm (GA). The optimization algorithm will determine the optimal model structures for accurate component modeling. Also, a parametric study was conducted to select the best model structure and validate the optimization process results. The second level of optimization is System Performance Optimization (SPO). The SPO was conducted by creating an optimization algorithm that will integrate the output of all the created component models. The output of the data-driven models will be optimized to be eventually used to optimize the set points of the whole system. The setpoints that were chosen to be optimized for this paper are the supply air temperature (SA) and the static duct pressure (PS). The testing results show that the proposed models can accurately predict system performance. Moreover, the two-level optimization process has resulted in a 25% and 22% savings in the fan power consumption and the reheat energy, respectively, when compared against the baseline case that was selected for this study. Also, this paper has validated the use of such data-driven models that can help in developing several aspects of the industry.
Citation: 2021 Virtual Conference Papers
Product Details
- Published:
- 2021
- Number of Pages:
- 9
- Units of Measure:
- Dual
- File Size:
- 1 file , 1.9 MB
- Product Code(s):
- D-VC-21-C060
