Reiter, Elmar R., authorSheaffer, J. D., authorCochrane, H., authorCook, J., authorJohnson, G. R., authorLeong, J., authorNakagawa, T., authorColorado State University, publisher2016-03-152016-03-151982http://hdl.handle.net/10217/171096Includes bibliographical references (page 41).Includes other related bibliographical references.March 1982.Our weather dependent model of energy demand for space heating has been applied to estimate total daily demand for an entire region. Preliminary tests using a simplified version of the model have yielded results with accuracies comparable to those obtained previously for individual communities. Intraregional subareas exhibited considerable diversity in rates of demand, factors controlling demand and in overall accuracy of model predictions. Progress toward obtaining detailed building census and meteorological data for physical and physical-reference modelling of regional space heating energy requirements is also described. A physical model of energy demand for air conditioning in large buildings has received preliminary testing. The Group Method of Data Handling (GMDH) algorithm that has consistently provided adequate parameterization for modular models of the space heating needs for an array of building types failed in its first application to air conditioning data. The reasons for this failure are complex and not completely resolved. Presently however, this failure is viewed as an opportunity to obtain additional basic insight into weather-energy demand relationships. A model of local climate variability that considers both topography and urban "heat island" effects has been refined to an advanced state of development. This climate modelling capability will allow regional scale physical and physical-reference modelling of energy demand to proceed by reducing the requirements for mesoscale observational networks to obtain energy demand-weighted meteorological data for each city. The potential value of improved weather forecast information to a large electric generation and transmission utility company has been studied. A simple weather-driven demand model for electricity uses weather forecasts to predict hour-by-hour loads, one day in advance. These load forecasts are in turn used by a system scheduling model to determine the most economical generating configuration to meet forecast demands. The results demonstrate significant potential value for improved accuracy of weather forecast services. Lastly, we have examined the actual cost effectiveness of structural modifications and system retrofits for reducing energy consumption for space conditioning in large buildings. Data obtained for five rather diverse alterations suggest that considerable broad-based conservation potential is available through applications of improved energy utilization technology.reportsengEnergy conservation -- ColoradoEnergy consumption -- Climatic factors -- ColoradoAtmospheric temperature -- ColoradoText