Time at Temperature and Simulation Models

If a bin method is used, with regression analysis based on outside air temperature, the best and most readily available energy analysis methodology is to use historical or standard weather data. It is important that average weather data is not used exclusively, since it would dramatically lessen the effects of extreme weather. For example, the temperature may frequently reach 100°F (38°C) in areas where the average temperature is 75°F (24°C). In the case of a chilled water system analysis, for example, use of average figures would lower the calculated peaks in both chilled water requirements and chiller power.

The local weather data station, NOAA, ASHRAE, publishers of modeling software, and various equipment manufacturers are all potential sources of weather data (Typical Meteorological Year, Test Reference Year). In addition, many utilities now closely monitor weather in their service territory, which can be made available to customers. Typically, it is possible to obtain, at a minimum, 5°F (about 3°C) bin weather data showing the number of hours in each month during which the temperature is in the 5°F bin range. In the best case, this is further split to 1°F (or 0.6°C) increments and also provides the time-of-day of the temperature occurrence. The best available weather data should be used to generate predictable cool ing and heating loads and relationships of system performance with varying loads. In addition to establishing the number of hours at each load with weather data, hours at load will be further differentiated by utility rate period when necessary.

Difficulties can arise because this use of bin data results in a static representation. Analysis is complicated by the lag in a building's thermal response to changing temperature conditions. Facilities with substantial mass may experience a thermal time lag of several hours. A peak temperature at 2:00 p.m., for example, could result in a peak cooling requirement at 4:00 p.m. Conversely, as outside air temperatures are reduced, peak cooling requirements may persist for several hours due to stored internal heat gains. Facilities with high ventilation rates will be less subject to such impacts, but most facilities will experience some thermal time-constant influence. Care must be taken to evaluate the regression data to establish a level of certainty in the results. It is often necessary to make an assessment of what additional independent variables, such as facility operational or production schedules, will be used as multiple regression coefficients. Also, unless the data is already separated, it is difficult to assign hours to discrete energy rate periods. Still, this format is very useful for energy savings verification purposes.

To perform more dynamic analyses, hourly computer simulation models are used. The building construction and system performance characteristics are input to the simulation model, which applies these characteristics to determine load conditions over every hour of the year. Time constants, thermal lag factors, operation and production schedules, and other influential features are modeled in such programs and the interactive effects of numerous measures are simultaneously considered. When considering multiple replacement options or other system improvements (e.g., chilled water reset), modeling, while generally more time-consuming, is quite valuable.

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