ASHRAE Journal - December 2010 - 131

the most extreme months are rated and marked, the year remaining with unmarked months is then chosen as the representative typical weather year. This methodology excludes extremes, constructing the typical year from milder data. After TRY, ASHRAE constructed the WYEC and IWEC data sets, which include solar insolation data. As a solution to finding a better typical weather year that includes typical extremes as well as averages, NCDC and Sandia National Laboratory constructed the TMY selection method and data set. Instead of choosing a single year of actual weather data, each month from a given multiyear sample of weather data was rated by solar radiation, dry-bulb and dew-point temperature, and wind speed relative to the long term distributions of these climatic factors over the multiyear period. The months most similar to the long-term distribution were chosen. Also, the data of TMY includes solar insolation.2 With so many options as input for typical weather year, does choice of weather data affect building simulation? In a comparison of TRY, TMY, TMY2, TMY3, and WYEC selection methods, the weather models constructed correlated well with the averages for the reference 30 years of observed hourly weather data. For dry-bulb and dew-point temperatures, and global horizontal irradiance, the R squared values were between 0.97 and 0.99 or better. None of the weather data did well with predicting direct normal irradiance.3 Seo, et al., found that when a typical weather year is chosen as outlined by TMY and IWEC protocol from 30 years of observed weather data, the resulting peak energy demand analyses differ by at most 5%. Nonetheless, TMY selection methods obtain results closer to simulations using the averages of the 30 years of observed data.1 When the same building simulation was run for varying typical weather year data sets (TRY, TMY and TMY2, WYEC and WYEC2) and compared against a simulation using the average from 30 years of observed weather data (1961–1990), it was found that TRY varied the most from the 30-year averages. The TMY simulated solar insolation values were close to the 30year averages and the WYEC simulations closely followed the average design temperatures and degree days. Neither WYEC nor TMY outperformed the other.2 The reduction of a building’s energy consumption by using model predictive control depends on the accuracy of the control system’s weather forecasting. Model Predictive Control (MPC) systems use various algorithms and time horizon lengths to predict future weather from externally or locally collected weather data. A comparison of Simple Prior Moving Average (SPMA), Exponentially Weighted Moving Average (EWMA), and two types of Neural Network (NN) time series algorithms showed that models using SPMA or EWMA are less complex and costly to implement and operate, and are more accurate than NNs. When a model based on each technique was used to predict Dry-Bulb Temperature (DBT), Global Horizontal Irradiance (GHI), and Relative Humidity (RH), it was found that GHI was
December 2010

best predicted by EWMA models, DBT by SPMA models, and RH was well-predicted by all models. DBT is among the most important weather factors for determining building energy use and was very poorly predicted by the focused time delay NN. Overall, it was shown that less-complex time series models outperformed the more complex NN models for the six-hour horizon required to perform MPC.4

energy saving Potential
Real-time and forecast weather information is an essential element of several energy-saving equipment and operational strategies that are the subject of ongoing research and early product introductions. Operational strategies that can be used to control HVAC systems include Rule-Based Control (RBC) and Model Predictive Control (MPC). RBC manages the building’s HVAC system based on predefined responses for given sets of conditions. MPC uses a weather prediction algorithm in concert with building energy simulation and thermal comfort requirements to determine the HVAC load and load adjustments for a building over a given time horizon. Based on the predicted load requirements and thermal comfort requirements of the building, the control system most effectively uses the HVAC components to heat and cool the building, while trying to minimize energy used.5 If the control system can predict the weather conditions, the HVAC system and thermal storage properties of the building shell can be used most effectively to maintain desired indoor air temperature and quality while using the least energy. The lynchpin of weather-based predictive control is forecasting. Zavala, et al., studied the difference between empirical and physics-based weather modeling. A comparison of the empirical Gaussian Process (GP) model and the physics based Weather Research and Forecast (WRF) model found that the WRF provides more useful 24 hour forecasts for MPC control. Its errors are less than or equal to 5°C (9°F), and its uncertainty bounds contain the true temperature. Unlike WRF, GP’s uncertainty bounds do not accurately contain the actual ambient temperature, leading to overshoots of the thermal comfort zone. This demonstrates that for this application, WRF is the better choice. GP and other empirical models capture the periodicity of the climate observed and are less computationally expensive and equally accurate when shorter time horizons are used.6 By using a building’s thermal mass for energy storage, heating and cooling demands can be shifted to off-peak electricity hours. MPC can determine when to begin night cooling of a building, or heating, based on predicted weather. MPC has successfully shifted heating and cooling demands to off-peak hours.6 One building controls manufacturer, in collaboration with the Swedish Meteorological and Hydrological Institute, studied the use of weather data to control energy supply to a building’s HVAC system. The results demonstrated savings of 20 kWh/ m2·yr (6.3 kBtu/ft2·yr), or 10% of heating costs in the homes studied in Sweden. The control unit requires hourly weather
ASHRAE Journal 131



ASHRAE Journal - December 2010

Table of Contents for the Digital Edition of ASHRAE Journal - December 2010

ASHRAE Journal - December 2010
Contents
Commentary
Industry News
Letters
Meetings and Shows
Q&A: New HFO-Based Option
Feature Articles
Optimizing Building Controls During Commissioning
Radiant Slab Cooling for Retail
Data Center Retrofit: Heat Containment and Airflow Management
Green Call Center
Building Sciences
New Product Preview
Commissioning
Emerging Technologies
Solar NZEB Project
IAQ Applications
Engineer’s Notebook
Special Products
Washington Report
Products
2010 ASHRAE Journal Indices
Classified Advertising
Advertisers Index
ASHRAE Journal - December 2010 - ASHRAE Journal - December 2010
ASHRAE Journal - December 2010 - Cover2
ASHRAE Journal - December 2010 - 1
ASHRAE Journal - December 2010 - 2
ASHRAE Journal - December 2010 - Contents
ASHRAE Journal - December 2010 - Commentary
ASHRAE Journal - December 2010 - 5
ASHRAE Journal - December 2010 - Industry News
ASHRAE Journal - December 2010 - 7
ASHRAE Journal - December 2010 - 8
ASHRAE Journal - December 2010 - 9
ASHRAE Journal - December 2010 - Letters
ASHRAE Journal - December 2010 - 11
ASHRAE Journal - December 2010 - 12
ASHRAE Journal - December 2010 - 13
ASHRAE Journal - December 2010 - Meetings and Shows
ASHRAE Journal - December 2010 - 15
ASHRAE Journal - December 2010 - Q&A: New HFO-Based Option
ASHRAE Journal - December 2010 - 17
ASHRAE Journal - December 2010 - 18
ASHRAE Journal - December 2010 - 19
ASHRAE Journal - December 2010 - 20
ASHRAE Journal - December 2010 - 21
ASHRAE Journal - December 2010 - Optimizing Building Controls During Commissioning
ASHRAE Journal - December 2010 - 23
ASHRAE Journal - December 2010 - 24
ASHRAE Journal - December 2010 - 25
ASHRAE Journal - December 2010 - 26
ASHRAE Journal - December 2010 - 27
ASHRAE Journal - December 2010 - Radiant Slab Cooling for Retail
ASHRAE Journal - December 2010 - 29
ASHRAE Journal - December 2010 - 30
ASHRAE Journal - December 2010 - 31
ASHRAE Journal - December 2010 - 32
ASHRAE Journal - December 2010 - 32a
ASHRAE Journal - December 2010 - 32b
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ASHRAE Journal - December 2010 - 35
ASHRAE Journal - December 2010 - 36
ASHRAE Journal - December 2010 - 37
ASHRAE Journal - December 2010 - 38
ASHRAE Journal - December 2010 - 39
ASHRAE Journal - December 2010 - Data Center Retrofit: Heat Containment and Airflow Management
ASHRAE Journal - December 2010 - 41
ASHRAE Journal - December 2010 - 42
ASHRAE Journal - December 2010 - 43
ASHRAE Journal - December 2010 - 44
ASHRAE Journal - December 2010 - 45
ASHRAE Journal - December 2010 - 46
ASHRAE Journal - December 2010 - 47
ASHRAE Journal - December 2010 - 48
ASHRAE Journal - December 2010 - 49
ASHRAE Journal - December 2010 - Green Call Center
ASHRAE Journal - December 2010 - 51
ASHRAE Journal - December 2010 - 52
ASHRAE Journal - December 2010 - 53
ASHRAE Journal - December 2010 - 54
ASHRAE Journal - December 2010 - 55
ASHRAE Journal - December 2010 - Building Sciences
ASHRAE Journal - December 2010 - 57
ASHRAE Journal - December 2010 - 58
ASHRAE Journal - December 2010 - 59
ASHRAE Journal - December 2010 - 60
ASHRAE Journal - December 2010 - 61
ASHRAE Journal - December 2010 - 62
ASHRAE Journal - December 2010 - New Product Preview
ASHRAE Journal - December 2010 - 64
ASHRAE Journal - December 2010 - 65
ASHRAE Journal - December 2010 - 66
ASHRAE Journal - December 2010 - 67
ASHRAE Journal - December 2010 - 68
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ASHRAE Journal - December 2010 - 97
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ASHRAE Journal - December 2010 - 111
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ASHRAE Journal - December 2010 - 120
ASHRAE Journal - December 2010 - 121
ASHRAE Journal - December 2010 - 122
ASHRAE Journal - December 2010 - 123
ASHRAE Journal - December 2010 - 124
ASHRAE Journal - December 2010 - Commissioning
ASHRAE Journal - December 2010 - 126
ASHRAE Journal - December 2010 - 127
ASHRAE Journal - December 2010 - 128
ASHRAE Journal - December 2010 - 129
ASHRAE Journal - December 2010 - Emerging Technologies
ASHRAE Journal - December 2010 - 131
ASHRAE Journal - December 2010 - 132
ASHRAE Journal - December 2010 - 133
ASHRAE Journal - December 2010 - Solar NZEB Project
ASHRAE Journal - December 2010 - 135
ASHRAE Journal - December 2010 - 136
ASHRAE Journal - December 2010 - 137
ASHRAE Journal - December 2010 - IAQ Applications
ASHRAE Journal - December 2010 - 139
ASHRAE Journal - December 2010 - 140
ASHRAE Journal - December 2010 - 141
ASHRAE Journal - December 2010 - Engineer’s Notebook
ASHRAE Journal - December 2010 - Special Products
ASHRAE Journal - December 2010 - Washington Report
ASHRAE Journal - December 2010 - 145
ASHRAE Journal - December 2010 - Products
ASHRAE Journal - December 2010 - 147
ASHRAE Journal - December 2010 - 148
ASHRAE Journal - December 2010 - 149
ASHRAE Journal - December 2010 - 2010 ASHRAE Journal Indices
ASHRAE Journal - December 2010 - 151
ASHRAE Journal - December 2010 - 152
ASHRAE Journal - December 2010 - 153
ASHRAE Journal - December 2010 - 154
ASHRAE Journal - December 2010 - 155
ASHRAE Journal - December 2010 - 156
ASHRAE Journal - December 2010 - Classified Advertising
ASHRAE Journal - December 2010 - 158
ASHRAE Journal - December 2010 - 159
ASHRAE Journal - December 2010 - Advertisers Index
ASHRAE Journal - December 2010 - Cover3
ASHRAE Journal - December 2010 - Cover4
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