Adding sub-hourly occupancy prediction, occupancy-sensing control and manual environment control to whole-building energy simulation

A wide range of events in buildings occur at sub-hourly frequencies. Notable examples include daylight-sensing control and manual adjustment of blinds and lights in response to sub-hourly illuminance variations. Such short-term changes can produce notable shifts in instantaneous solar and equipment loads, in turn affecting electrical energy demand. Although sub-hourly time discretization is currently available in several whole building energy simulation programs, it remains challenging to model this level of complexity as traditional hourly utilization or diversity profiles describing occupancy, lighting and equipment loads remain the basic input data model. This paper provides a background review of models predicting occupancy, occupancy-sensing control and manual environmental control, and outlines their current addition to whole building energy simulation, in particular ESP-r. INTRODUCTION Sub-hourly time discretization presents an opportunity to reconsider several existing assumptions in building energy simulation. An example is the use of hourly meteorological input data. Walkenhorst et al. (2002) demonstrate that the predicted annual artificial lighting demand can be underestimated by up to 27% if daylighting simulations are based on 1-hour means instead of 1-min means of measured beam and diffuse irradiances. To this end, an adapted Skartveit and Olseth (1992) stochastic model, deriving short-term fluctuations from hourly time series irradiance data, is found in DAYSIM (Reinhart 2001), a RADIANCE-based (Ward 1994; Ward Larson et al. 1998) dynamic daylight simulation method. Similar work is described in Janak and Macdonald (1999). Analogous work on stochastic modelling of short term wind velocity fluctuations is presented by Marques da Silva and Saraiva (2002). A number of these models should likely find their way in larger whole building energy simulation programs in the near future. This paper deals with another assumption that may need revising in the light of sub-hourly time discretization: how occupancy-related input data models are defined and used in whole-building energy simulation. Although whole building energy simulation programs such as ESP-r (ESRU 1999; Clarke 2001) or EnergyPlus (Crawley et al. 2001) offer sub-hourly simulation time-steps, diversity profiles of occupancy and related internal gains, such as lighting and equipment, constitute the main input data model; a solution passed down from the previous generation of hourly simulation programs. It is nevertheless possible in ESP-r to access sub-hourly input data through external files or databases, but this approach is usually appropriate for short, detailed test cell studies with measured data. There are at least three major impediments to extending the use of this approach to annual energy simulations: 1. this would require pre-configuring data for many variables (various casual gains, optical sets for multiple window/blind configurations, etc.) for multiple zones, a risky and time-consuming exercise; 2. the approach is essentially limited to input of meteorological, casual gain and mass flow data, i.e. other external variable input is simply not possible without major changes to the source code; and most importantly, 3. the approach does not allow control over input, a potentially desirable option notably in cases where control might depend on the state of certain variables known only at run-time, e.g. room air temperature. As sub-hourly discretization increasingly becomes the norm, a more robust and integrated solution seems desirable. Denis Bourgeois (denis.bourgeois@arc.ulaval.ca) is a PhD student, Ecole d'architecture, Universite Laval, Quebec, Canada; Dr. Jon Hand (jon@esru.strath.ac.uk) and Dr. Iain Macdonald (iain@esru.strath.ac.uk) are research fellows at the Energy Systems Research Unit (ESRU), University of Strathclyde, Glasgow, Scotland; Dr. Christoph Reinhart (christoph.reinhart@nrccnrc.gc.ca) is a research officer, Institute for Research in Construction, National Research Council Canada, Ottawa, Canada.