Showing papers on "Occupancy published in 1996"

The Neurothermostat: Predictive Optimal Control of Residential Heating Systems

Abstract: The Neurothermostat is an adaptive controller that regulates indoor air temperature in a residence by switching a furnace on or off. The task is framed as an optimal control problem in which both comfort and energy costs are considered as part of the control objective. Because the consequences of control decisions are delayed in time, the Neurothermostat must anticipate heating demands with predictive models of occupancy patterns and the thermal response of the house and furnace. Occupancy pattern prediction is achieved by a hybrid neural net/look-up table. The Neurothermostat searches, at each discrete time step, for a decision sequence that minimizes the expected cost over a fixed planning horizon. The first decision in this sequence is taken, and this process repeats. Simulations of the Neurothermostat were conducted using artificial occupancy data in which regularity was systematically varied, as well as occupancy data from an actual residence. The Neurothermostat is compared against three conventional policies, and achieves reliably lower costs. This result is robust to the relative weighting of comfort and energy costs and the degree of variability in the occupancy patterns.

Measured Field Performance and Energy Savings of Occupancy Sensors: Three Case Studies.

Abstract: Occupancy sensors have the potential to significantly reduce energy use by switching off electrical loads when a normally occupied area is vacated. While occupancy sensors can be used to control a variety of load types, their most popular use has been to control lighting in commercial buildings. Manufacturers claim savings of 15% to 85%, although there is little published research to support the magnitude or timing of reductions. Energy savings and performance are directly related to the total wattage of the load being controlled, effectiveness of the previous control method, occupancy patterns within the space and proper sensor commissioning. In an effort to measure performance, energy savings, and occupant acceptance, occupancy sensors were installed in a small office building and two elementary schools. 15-minute data was collected to assess performance. The three sites varied not only in size but also by occupancy patterns, occupant density, and the previous manual control strategies. Aggregate time-of-day lighting load profiles are compared before and after the installation and throughout the commissioning period when the sensors are tuned for optimum performance. For instance, savings on weekdays in the office building were less than 10% prior to the commissioning, although nearly doubled by proper tuning of the time delay setting and correcting false triggering problems. False ‘‘ons’’ during evening hours also affected savings. Occupant acceptance, sensor performance, and commissioning aspects are discussed as well as some recommendations for improved performance.

Factors Affecting Vehicle Occupancy Measurement

Abstract: Factors affecting vehicle occupancy measurement were examined with the aim of improving state vehicle occupancy monitoring programs. A comparison was conducted of five data sets looking at the effects on average vehicle occupancy (AVO) of time of day, day of week, road types, HOV lanes, locational differences, and traffic volume. It was found that AVO was higher in the afternoons, on Saturdays, and on HOV lanes. Inconsistent differences were found for the other variables, though there were considerable locational variations. Based on these factors, suggestions are made for drawing samples to represent regional and corridor-level vehicle occupancy levels.

Estimating Average Automobile Occupancy From Accident Data in New York State

Abstract: Average automobile occupancy (AAO) data are valuable input to congestion management systems (CMS). Continuous field collection of these data at the system level has been lacking because of high costs associated with current data collection methodology. It is shown how the New York State Department of Transportation (NYSDOT) has built upon prior research by the Connecticut Department of Transportation, which uses traffic accident data to calculate estimates of vehicle occupancy, and has tailored the process to meet NYSDOT's CMS needs. Accident data covering a 3-year period are used to estimate AAOs by county, year of occurrence, month of year, day of week, and time-of-day intervals. Occupancy rates are calculated to be lowest during the morning peak traffic period and highest during the evening period between 6:00 and 11:00 p.m. Occupancy rates are highest for summer months and lowest for winter months. Occupancy rates are highest for the weekends and lowest for weekdays. Accident-based AAOs are compared t...