Conference Year

2016

Keywords

optimal start, night setback, simulation

Abstract

Night setback is a common strategy used to reduce energy use in buildings. It involves increasing the cooling setpoint and decreasing the heating setpoint in a zone during unoccupied periods. To ensure occupant comfort and maximize energy savings, the zone temperature must be returned to the range defined by the occupied cooling and heating setpoints at occupancy, but not before. The time required to cool down or warm up a zone from a night setback condition is referred to as the return time and algorithms for predicting return time are commonly referred to as optimal start algorithms. Optimal start algorithms generally employ a model for predicting return time. This study describes the selection of separate return time models for cooling (i.e., a model for predicting the return time when cooling is required) and heating from 57 candidates. The following model forms were considered: τ = f (Tf - Ti), τ = f ((Tf - Ti), u), τ = f ((Tf - Ti), Tout), and τ = f ((Tf - Ti), u, Tout) where τ is the return time, Tf is the zone temperature at the end of the optimal start period, Ti is the zone temperature at the beginning of the optimal start period, u is exponentially weighted moving average (EWMA) of the zone cooling or heating demand at the beginning of the optimal start period, and Tout is the outdoor air temperature at the beginning of the optimal start period. Computer simulations were used to generate year-long data sets relating return time to the model inputs. The simulations considered the influence of climate, building mass, controller tuning, zone orientation, and the unoccupied control strategy on the return time. In all, 140 cooling data sets and 104 heating data sets were generated. For each data set, least squares regression was performed to determine the parameters for each of the 57 models considered. The performance of each model was quantified using the average root mean square prediction error across all simulations. The study revealed that the best models for predicting return time use the zone temperature change and the EWMA of the zone cooling or heating demand as inputs. The EWMA of the zone cooling or heating demand provides an indication of the recent history of the cooling or heating load on a zone and can account for intermittent cooling or heating that is required to keep the zone temperature within the bounds of the unoccupied setpoints. Notably, outdoor air temperature, a common input in optimal start algorithms, is not used. To the best of the authors' knowledge, zone cooling and heating demand have not been previously used as an input in an optimal start algorithm. The full paper will provide a detailed description of the simulations and model comparison undertaken in this study.

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