An introduction to data envelopment analysis , An introduction to data envelopment analysis , کتابخانه دیجیتال جندی شاپور اهواز

This paper reports on the design and implementation of a short-run forecasting model of hourly system loads and an evaluation of the forecast performance. The model was applied to historical data for the Puget Sound Power and Light Company, who did a comparative evaluation of various approaches to forecasting hourly loads, for two years in a row. The results of that evaluation are also presented here. The approach is a multiple regression model, one for each hour of the day (with weekends modelled separately), with a dynamic error structure as well as adaptive adjustments to correct for forecast errors of previous hours. The results show that it has performed extremely well in tightly controlled experiments against a wide range of alternative models. Even when the participants were allowed to revise their models after the first year, many of their models were still unable to equal the performance of the authors' models.

In recent years, a number of formal diagnostic tests for identifying misspecification of models and criteria for comparing alternative models have been proposed. Not many of them, however, have found common use among energy analysis. This paper provides a comprehensive listing of these techniques and describes them in a manner easily accessible to modelers of energy demand. The methods suggested are illustrated with an application to the modeling of peak electricity demand in a utility service area in the upper midwest.

This paper examines the impact of monetary and fiscal policy when supply-side effects of prices as well as exchange rates are taken into account in an open economy macro model. Partial deficit financing and imperfect international capital movements are appropriately modelled. It is shown that the well known Mundell–Fleming result, that fiscal policy is completely ineffective under perfect capital mobility and flexible exchange rates, is significantly affected when exchange rate effects on the supply side are taken into account. The paper also shows that such effects significantly alter the effects of devaluation under fixed exchange rates.

It is well known that a linear combination of forecasts can outperform individual forecasts. The common practice, however, is to obtain a weighted average of forecasts, with the weights adding up to unity. This paper considers three alternative approaches to obtaining linear combinations. It is shown that the best method is to add a constant term and not to constrain the weights to add to unity. These methods are tested with data on forecasts of quarterly hog prices, both within and out of sample. It is demonstrated that the optimum method proposed here is superior to the common practice of letting the weights add up to one.

Because utilities bill their residential and commercial customers by cycle on each working day of the month, the calculation of weather variables to associate with monthly sales data is complicated. We examined three different methods of calculating weather variables. 1.(1) For a utility that bills monthly, the most appropriate method is to calculate daily weather measures, then take a weighted sum of these daily measures over the current and previous month, with the weights for each day being proportional to the number of customers whose consumption on that day is billed in the current month. When weather variables are calculated in this way, accurate econometric models of electricity sales can be estimated.2.(2) If data on the number of customers in each cycle are unavailable, the first procedure can be applied under an assumption concerning the number of customers consuming on each day. For the three utilities in the study, using these approximate weights reduced the model accuracy noticeably but not substantially, implying: if data on the number of customers in each cycle can be retrieved, the effort expended in doing so will be rewarded with more accurate models; however, if such data are impossible to obtain, fairly accurate models can still be estimated.3.(3) The easiest method for calculating weather variables is to ignore the billing cycle phenomenon and take an unweighted sum of daily weather measures over days in the previous or current, or both, months. Our estimation results indicate that these simple measures decrease the accuracy of the models substantially, implying that the additional effort required to calculate weather variables that reflect the billing cycle phenomenon is clearly worthwhile in terms of increased model accuracy.

A two-step time-series and cross-section model is used to estimate time-of-day (TOD) demand for electricity or natural gas and to demonstrate an efficient computational method. Post-sample validation of the model results in several regions found average forecast errors in the aceptable range of 4.5% to 15%. Future hourly electricity-demand forecasts made for 32 regions using an historical trend scenario and a Data Resources Inc. macro model scenario indicate a good potential for the model. 12 references. (DCK)

The models of aggregate economic behavior that represent short-period analysis demonstrate that the necessary condition for full employment is that the level of investment generated in an economy be offset by the volume of saving made at the full employment income. This conclusion is based on the assumption that the labor force and the productive capacity of a given economy are fixed. In the short run these assumptions are realistic but labor force as well as productive capacity change over time. Population may be increasing and therefore what is full employment today may not be full employment tomorrow. Secondly, investment in a given year adds to the productive capacity of the economy thereby increasing its potential output. It is this dual aspect of investment (i. e. investment on the one hand offsetting saving and on the other increasing productive capacity) that is central in theories of economic growth. A second reason why productive capacity might increase is technical progress. Improvements in technology bring about increases in the efficiency of the factors of production thereby raising potential output.