Nov 222011

Recently I fielded a call from an equipment engineer in Minneapolis regarding a HVAC modeling project. He asked about my experience working with ‘Typical┬áMeteorological Year” (TMY) data sets. I’ve modeled a number of Heating, Cooling and Ventilation jobs were I needed the fine detail that TMY data sets provide. I told him the problem I had was using a spreadsheet. I found 8760 lines made for some unwieldy spreadsheets. Instead I put the TMY data in a database and worked my models using a hybrid of database calculations and spreadsheet calculations. He asked where to get the data. It can be found from a number of different sources online. I have been using TMY2 and now TMY3 data sets* from the National Renewable Energy Laboratory (NREL), other sources include…

  • TRY – ASHRAE’s Test Reference Year
  • WYEC – Weather Year for Energy Calculations
  • IWEC – International Weather for Energy Calculations
  • NCDC – National Climatic Data Center
  • TMY – Typical Meteorological Year

According to D.B. Crawley’s paper, “Which weather data should you use for energy simulations of commercial buildings?“, either WYEC nor TMY will work fine. These data sets are pretty good, however you need to keep in mind- they fail at the temperature extremes. The data sets are designed to weed out extreme tempertures in order to create smoother data sets. This can be problematic if you’re also using the simulation to size your equipment. Having said that, these TMY based models will be far more accurately modelling energy usage than using bin hours. They are also better when running what-if scenarios. In order to truly model a building for energy use, particularly where humidity control comes into play it is essential to model the energy use for each hour in a typical year- all 8760 data points.

Detailed hour-by-hour modeling using hourly weather data sets has become commonplace in the evaluation of design alternatives and the design of HVAC systems for larger buildings. For residential and small commercial buildings, calculating design loads based on high and low design temperature is still common practice. The economic issue is when the added cost of the more involved hour-by-hour modeling exercise can be expected to be justified by helping to guide the selection of equipment that provides significantly better part-load performance, resulting in tangible benefits of lower total annual energy cost and better comfort control in the building.-December 2010 ASHRAE Journal.

Other uses of TMY data sets are to adjust set-points based on outdoor temperature to control early morning pre-cooling. Both to take advantage of Lower temperatures and reduced peak demand rates. Building mass can also be used for energy storage. Off-peak heating and night cooling can be based on TMY modeling; shifting HVAC demands to off-peak hours and lower energy rates.

Some of this predictive control is finding its way down to the residential market with smart thermostats. I predict we’ll be seeing more of these smart thermostats… “A trial in 2000 households by Oncor Utilities in Texas resulted in heating and air-conditioning power cuts of 20% to 30% and annual savings up to $400. It also achieved complete AC turnoff at peak hours due to pre-cooling. These examples indicate that approximately 10% of energy to condition buildings can be potentially be saved by the use of control algorithms using forecaster weather conditions.”

*Note: The TMY3s are data sets of hourly values of solar radiation and meteorological elements for a 1-year period. Their intended use is for computer simulations of solar energy conversion systems and building systems to facilitate performance comparisons of different system types, configurations, and locations in the United States and its territories. Because they represent typical rather than extreme conditions, they are not suited for designing systems to meet the worst-case conditions occurring at a location. -NREL

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