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This paper addresses the problem of system identification for heating, ventilation, and air conditioning (HVAC) systems using a relatively small amount of data for the zone under consideration, by leveraging larger datasets for similar zones. To this end, a hybrid machine learning approach is developed where a pre‐trained recurrent neural network (RNN) model, trained on a large amount of data from a representative zone, is leveraged to build models for the other zones using a smaller amount of data. This is achieved by developing a hybrid model that integrates the pre‐trained RNN model with the models built using the subspace identification (SubID) technique to predict the residuals (differences between the real outputs and the predicted outputs from the pre‐trained RNN model) in the other zones. The effectiveness of the proposed hybrid approach is shown using real data collected from a multi‐zone fitness centre. The results demonstrate the superior performance of the hybrid approach over the cases where individual RNN and SubID models are directly developed using only the data from the zones in question. [ABSTRACT FROM AUTHOR]

It has been established that combinations of increased ventilation, improved filtration, and other HVAC techniques can reduce the likelihood of airborne disease transmission in buildings. However, with only qualitative guidance, it is difficult for building managers to make informed decisions. Furthermore, the possible actions almost always require additional energy consumption, which is generally not well characterized. To address this knowledge gap, we propose simplified physics-based models that can be used to quantify both the expected transmission rate and the associated energy consumption that result from HVAC system operation. By formulating all disinfection mechanisms in terms of "equivalent outdoor air", a common basis is established for comparing and combining different strategies. The transmission rate can thus be modeled by considering the airborne concentration of infectious particles that would result from an infector in the space. Energy consumption is then estimated by considering the change in HVAC variables and applying standard analysis. To illustrate the insights provided by these models, we present examples of how the proposed analysis can be applied to specific spaces, highlighting the fact that underlying transmission risk and energy-optimal disinfection strategies can vary significantly based on space characteristics. [ABSTRACT FROM AUTHOR]

Due to time-varying utility prices, peak demand charges, and variable-efficiency equipment, optimal operation of heating ventilation, and air conditioning systems in campuses or large buildings is nontrivial. Given forecasts of ambient conditions and utility prices, system energy requirements can be reduced by optimizing heating/cooling load within buildings and then choosing the best combination of large chillers, boilers, etc., to meet that load while accounting for switching constraints and equipment performance. With the presence of energy storage, utility costs can be further reduced by temporally shifting production, which adds an additional layer of complexity. Furthermore, due to changes in market and weather conditions, it is necessary to revise a given schedule regularly as updated information is received, which means the problem must be tractable in real time (e.g., solvable within 15 min). In this paper, we present a mixed-integer linear programming model for this problem along with reformulations, decomposition approaches, and approximation strategies to improve tractability. Simulations are presented to illustrate the effectiveness of these methods. By removing symmetry from identical equipment, decomposing the problem into subproblems, and approximating longer-timescale behavior, large instances can be solved in real time to within 1% of the true optimal solution. [ABSTRACT FROM AUTHOR]