Wednesday, 26 January 2011: 5:00 PM
4C-2 (Washington State Convention Center)
Demand for energy has a strong dependence on temperature, due to the power demands of residential and commercial heating and air conditioning. Modeling inputs for prediction of energy demand have generally been based on the (mainly temperature) observed values and forecasts at airports. While this method gives reasonable results, improved results can be obtained by using geospatial information and mesoscale weather forecasting methodology. Key inputs for the model are land cover, mesoscale temperature forecasts and population. A population-weighted index is then created that provides a better model input than airport values. An example of this approach is presented for Atlanta, Georgia.
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