Weather Adjustment

The Hirst model was initialized in 1970. The winter that year was colder than normal in all regions except DOE9, and was hotter than usual during the summer in all regions except DOE4 and DOE6. The forecasts in Table 2 were not adjusted to account for abnormal weather during the initialization year. To adjust the model for 30-year average weather, space heating and air conditioning were scaled in each region by a proportion of 30-year average weather to 1970 weather.

In the original runs of the Hirst model, consumption forecasts were benchmarked to 1977 actual levels of consumption. Since 1977 was a colder than normal winter, the weather adjustment to 30-year average resulted in a lower level of consumption than the model run benchmarked to 1977 actual levels of consumption, as illustrated in the third column of Table 2. The base case run of the Hirst model resulted in a level of consumption approximately .165 quad more than with 30-year average weather conditions imposed. The adjustment for 1977 weather is not large. However, if the initialization year has unusual weather, the difference can be significant.

Moratorium on Residential Gas Hookups The market share equation for appliances assumes that consumers will base their appliance choice between electricity, natural gas, oil and propane upon income, equipment and fuel prices and appliance efficiency. The version of the model used in the initial simulation does not incorporate the moratorium on new residential gas hookups that have been occurring in all regions except DOE6 since 1972. RDFOR uses econometrically estimated equations using 1960-1975 data. Thus, a portion of the moratorium is incorporated in the forecasts. Hence, a true comparison of the Hirst model and RDFOR must include an adjustment in Hirst to reflect the moratorium.

A routine was added to the Hirst model code to prohibit new homes from using natural gas appliances in all regions except DOE6. Because of the way the switch was implemented, the relative growth rates of electricity and oil consumption were the same as in a model run without a moratorium. Thus, the switch from natural gas was mainly into electricity as illustrated in the fourth column of Table 2. This simulation is reported without adjusting the model for normal weather conditions. In the moratorium case only, forecasted natural gas consumption was down 1.16 quads in 1990 for the U.S. using Hirst, and electricity and oil were up .7 quads and .3 quads, respectively. Therefore, total end-use consumption was down .1 quad.

Revised Simulations - The last column of Table 2 shows the result of running the model to incorporate both adjustments. These figures reflect modifications in the Hirst model to make it comparable to RDFOR, including definitional modifications, regional elasticity, weather and gas moratorium adjustments. Comparison of column five with column one, the

RDFOR base figures, shows that there is only a small difference of .6 quads. Projected natural gas consumption would be reduced slightly by .04 quads. Oil consumption would increase slightly by .18 quads. The major projected change would be an increase in electricity consumption by .67 quads using the fully modified Hirst model.

The combined adjustments in the modified Hirst decrease the original Hirst-RDFOR difference in 1990 natural gas consumption from 1.2 quads to -.04 quads. However, electricity consumption which had only been approximately equal between the two versions of the model is now .67 quads higher.

This paper has reviewed the importance of model assessment in integrating two models. The assessment process reveals critical factors that must be known before the integration process can be initiated. The assessment process also provides a guide for modifying models to achieve consistency required for integration. The importance of model integration for policy analysis was also reviewed. The integration process was illustrated by reporting the procedures used to incorporate the Hirst model into MEFS. The work that was begun in integrating the Hirst model into the MidRange Energy Demand Forecasting System is continuing both at Oak Ridge (ORNL) and at EIA. The effort at ORNL is designed to improve the internal structure of the model, while the staff at EIA is attempting to improve the interface capability.

In summary, there were five major findings resulting from the task of integrating the Hirst Residential Energy Use Model into the MEFS. First, the use of repeated simulations to generate pseudo data to estimate reduced form energy demand curves is inferior both empirically and computationally to using single pertubations to estimate a narrow range arc elasticity. Second, the MEFS and the Hirst model were developed independently which explains the use of different data concepts in each model. These concepts were reconciled to adjust Hirst to RDFOR. Third, the treatment of abnormal weather conditions and, thus, relation to normal energy consumption patterns must be explicitly incorporated in all updates of the Hirst model. Fourth, the treatment of national gas curtailments in the Hirst model was not only necessary for the simulated comparisons with RDFOR, but served to illustrate the superiority of the Hirst model over RDFOR for use in policy analysis. Finally, the importance of the exogenous driving variables of housing stock, income, and population illustrate the need for improving this sector of the model.

REFERENCES

1. Hirst, Eric and Janet Carney, The ORNL Engineering Economic Model of Residential Energy Use, Oak Ridge National Laboratory, ORNL/CON 24, July 1978.

2. Elizabeth Chase MacRae, PIES: A User's Guide FEA/N-77/115 June, 1977 contains a general introduction to the PIES System, which was a forerunner of the current Mid-Range Energy Forecasting System.

This paper is a discussion of the validation process undertaken by the authors. It is not intended nor does it represent a policy statement of the Department of Energy or the Electric Power Research Institute.

PANEL SUMMATION

The final event of the Workshop was a panel summation that addressed the future of model assessment. The panelists were Martin Greenberger, William Hogan, George Lady, David Nissen, Richard Richels, and David Wood. The following is an edited version of the discussion.

DR. WOOD: I would have a few summary comments. First, with all of you, I have quite enjoyed the last two days and Saul's organization of this Workshop. Some things that I already knew have been reaffirmed. For example, whenever you organize an activity such as this, you must be sure to invite David Nissen and Larry Mayer to keep it lively, as well as informative.

Some issues discussed during the Workshop seemed especially provocative to me. John Weyant's discussion of model assessment versus the Forum, substitutes or complements, helped me to focus more clearly on what the differences between those two enterprises might be, and why in the future these apparently separate activities are likely to merge.

I thought Bud Cherry's observation yesterday that much of what goes on in the Forum process is not reportable in the traditional documentation and, in fact, internalized in the alumnae of that process, is suggestive for further organizational initiative. If the specific product of the Forum is policy research studies, then the value added is an alumnae sensitized to the uses of policy models in policy research.

Finally, I want to emphasize a view I have expressed several times during this Workshop. Policy model evaluation seems to me to be scientific analysis and peer review organized and presented to provide for the information requirements of all groups--not just modelers--involved in the policy research process. What may appear new or distinctive is the effort to satisfy non-modeler needs for information about models, their scientific validity and applicability to particular policy issues. As this evaluative aspect of the policy sciences matures, the apparent distinctions between scientific analysis and review and policy model evaluation will evolve into good scientific practice with whatever particular methods and practices are appropriate to satisfy the information needs of non-modelers involved in the policy process.

DR. GASS: Thank you Dave. Next, Martin Greenberger.

DR. GREENBERGER; My remarks can be very brief because I spoke a little bit earlier today. I would like to join with Dave in congratulating Saul on bringing together an excellent group of people. The National Bureau of Standards and the Department of Energy deserve our thanks for sponsoring this very constructive Workshop.

A member of the Workshop told me he felt the future of model assessment hinged on whether modeling is more like writing or physics. What he meant was that if it is like writing, then just as writing is going to continue to have its critics for a long time, so modeling will continue to have its model analysts.

But in physics, he did not see the analogy. I pointed out to him that there is in face a parallel in physics. There is the theoretical physicist, who corresponds to the modeler, and then there is the experimental physicist--the fellow in the laboratory testing the theories--who corresponds to the model analyst. In physics, many more theories are proposed than survive because of the efforts of the experimentalists.

The question is, can you have the same person serving both functions? In physics, the answer is generally no. Theoretical and experimental physicists tend to be different people. They work cooperatively, if not always harmoniously, and there is a very productive symbiosis between them. It seems to me that it is entirely possible for the same kind of symbiosis to develop as between model developers and model analysts for the benefit of the policy-makers and model users.

I am very much encouraged by what has taken place over the past four years. The field of policy modeling four years ago revealed a very clear deficiency which it has begun to fill with the development of model analysis. There is still a way to go, but now we are not asking the question, "What can we do?" but "What form will it take?" That is encouraging.

DR. GASS: Thank you Martin. Next, Bill Hogan.

DR. HOGAN: Well, the first thing I would like to do is to ask Larry Mayer if he has any plans for dinner? I don't have to catch a plane until 8 o'clock, so we can see if we can arrange something later on. This is to disprove his statements about his ostracism. So I told him privately his remarks are very provocative, but although I think almost everything he said as a factual matter is correct, the general thrust of his comments was not in keeping with what I viewed as the pleasant progress of the discussions of this workshop. That progress has been in laying out the heterogenous nature of models in use and the different kinds of applications and the different kinds of uses.

I won't repeat all of the taxonomies that were proposed. There were many and they are developing. If I may use an analogy we are trying to make the distinction here between the processes that physicists are involved in vs. the processes that lawyers are involved in. This is the distinction between policy research and policy analysis models. We are concerned about people using the rules of the physicist in a lawyer's game or using the practices of lawyers to claim the benefits of physics. We don't want to confuse these two activities.