Abstract
Most of the energy consumed by the residential and commercial buildings in the U.S. is dedicated to space cooling and heating systems, according to the U.S. Energy Information Administration. Therefore, the need for better operation mechanisms of those existing systems become more crucial. The most vital factor for that is the need for accurate models that can accurately predict the system component performance. Therefore, this paper’s primary goal is to develop a new accurate data-driven modeling and optimization technique that can accurately predict the performance of the selected system components. Several data-enabled modeling techniques such as artificial neural networks (ANN), support vector machine (SVM), and aggregated bootstrapping (BSA) are investigated, and model improvements through model structure optimization proposed. The optimization algorithm will determine the optimal model structures and automate the process of the parametric study. The optimization problem is solved using a genetic algorithm (GA) to reduce the error between the simulated and actual data for the testing period. The models predicted the performance of the chilled water variable air volume (VAV) system’s main components of cooling coil and fan power as a function of multiple inputs. Additionally, the packaged DX system compressor modeled, and the compressor power was predicted. The testing results held a low coefficient of variation (CV%) values of 1.22% for the cooling coil, and for the fan model, it was found to be 9.04%. The testing results showed that the proposed modeling and optimization technique could accurately predict the system components’ performance.
Highlights
In 2017, about 38 quadrillion British thermal units of the total U.S energy consumption was consumed by the residential and commercial sectors, according to the U.S Energy InformationAdministration
It was found that the model structure with number of neurons, the cooling coil component
It is noted that the results produced by the optimization tool are similar in value to those obtained in the parametric study
Summary
In 2017, about 38 quadrillion British thermal units of the total U.S energy consumption was consumed by the residential and commercial sectors, according to the U.S Energy InformationAdministration. In 2017, about 38 quadrillion British thermal units of the total U.S energy consumption was consumed by the residential and commercial sectors, according to the U.S Energy Information. Where it was found that in 2012, the space heating consumes most of the overall energy use in commercial buildings [1]. In addition to the electricity prices that are rapidly increasing and the increasing cost of operating the heating ventilation and air condition (HVAC) systems in the buildings, the buildings are responsible for 44.6% of the total CO2 emissions, which is the most significant portion compared to 34.3% for transportation and 21.1% for the industry sector. HVAC systems are heating, ventilation, and air conditioning systems that are responsible for heating and cooling the space as well as ventilation to maintain the inhabitant comfort levels.
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