Many fault detection, optimization, and control logic methods rely on sensor feedback that assumes the system is operating at steady-state conditions, despite persistent transient disturbances. While filtering and signal processing techniques can eliminate some transient effects, this paper proposes an equilibrium prediction method for first-order dynamic systems using an exponential regression. This method is particularly valuable for many commercial and industrial energy system, whose dynamics are dominated by first-order thermo-fluid effects. To illustrate the basic advantages of the proposed approach, Monte Carlo simulations are used. This is followed by three distinct experimental case studies to demonstrate the practical efficacy of the proposed method. First, the ability to predict the carbon dioxide level in classrooms allows for energy efficient control of the ventilation system and ensures occupant comfort. Second, predicting the optimal time to end the cool-down of an industrial sintering furnace allows for maximum part throughput and worker safety. Finally, fault detection and diagnosis (FDD) methods for air conditioning systems typically use static system models; however, the transient response of many air conditioning signals may be approximated as first-order, and therefore, the prediction model enables the use of static fault detection methods with transient data (a need that has not been addressed in over 20 years of air conditioning FDD research). In this paper, the equilibrium prediction method's performance will be quantified using both Monte Carlo simulations and case studies.