Short‐term forecasting of industrial electricity consumption in Brazil

This paper presents short‐term forecasting methods applied to electricity consumption in Brazil. The focus is on comparing the results obtained after using two distinct approaches: dynamic non‐linear models and econometric models. The first method, that we propose, is based on structural statistical models for multiple time series analysis and forecasting. It involves non‐observable components of locally linear trends for each individual series and a shared multiplicative seasonal component described by dynamic harmonics. The second method, adopted by the electricity power utilities in Brazil, consists of extrapolation of the past data and is based on statistical relations of simple or multiple regression type. To illustrate the proposed methodology, a numerical application is considered with real data. The data represents the monthly industrial electricity consumption in Brazil from the three main power utilities: Eletropaulo, Cemig and Light, situated at the major energy‐consuming states, Sao Paulo, Rio de Janeiro and Minas Gerais, respectively, in the Brazilian Southeast region. The chosen time period, January 1990 to September 1994, corresponds to an economically unstable period just before the beginning of the Brazilian Privatization Program. Implementation of the algorithms considered in this work was made via the statistical software S‐PLUS. Copyright © 1999 John Wiley & Sons, Ltd.     

When are Direct Multi‐step and Iterative Forecasts Identical?

Although both direct multi‐step‐ahead forecasting and iterated one‐step‐ahead forecasting are two popular methods for predicting future values of a time series, it is not clear that the direct method is superior in practice, even though from a theoretical perspective it has lower mean squared error (MSE). A given model can be fitted according to either a multi‐step or a one‐step forecast error criterion, and we show here that discrepancies in performance between direct and iterative forecasting arise chiefly from the method of fitting, and is dictated by the nuances of the model's misspecification. We derive new formulas for quantifying iterative forecast MSE, and present a new approach for assessing asymptotic forecast MSE. Finally, the direct and iterative methods are compared on a retail series, which illustrates the strengths and weaknesses of each approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Multi-step forecast error variances for periodically integrated time series

A periodically integrated (PI) time series process assumes that the stochastic trend can be removed using a seasonally varying differencing filter. In this paper the multi-step forecast error variances are derived for a quarterly PI time series when low-order periodic autoregressions adequately describe the data. The forecast error variances display seasonal variation, indicating that observations in some seasons can be forecast more precise than those in others. Two examples illustrate the empirical relevance of calculating forecast error variances. A by-product of the analysis is an expression for the seasonally varying impact of the stochastic trend.     

The use of non-time series information in sales forecasting: A case study

The contribution of product and industry knowledge to the accuracy of sales forecasting was investigated by examining the company forecasts of a leading manufacturer and marketer of consumable products. The company forecasts of 18 products produced by a meeting of marketing, sales, and production personnel were compared with those generated by the same company personnel when denied specific product knowledge and with the forecasts of selected judgemental and statistical time series methods. Results indicated that product knowledge contributed significantly to forecast accuracy and that the forecast accuracy of company personnel who possessed industry forecasting knowledge (but not product knowledge) was not significantly different from the time series based methods. Furthermore, the company forecasts were more accurate than averages of the judgemental and statistical time series forecasts. These results point to the importance of specific product information to forecast accuracy and accordingly call into question the continuing strong emphasis on improving extrapolation techniques without consideration of the inclusion of non-time series knowledge.     

Integrating time-series and end-use methods to forecast electricity sales

Two types of forecasting methods have been receiving increasing attention by electric utility forecasters. The first type, called end-use forecasting, is recognized as an approach which is well suited for forecasting during periods characterized by technological change. The method is straightforward. The stock levels of energy-consuming equipment are forecast, as well as the energy consumption characteristics of the equipment. The final forecast is the product of the stock and usage characteristics. This approach is well suited to forecasting long time periods when technological change, equipment depletion and replacement, and other structural changes are evident. For time periods of shorter duration, these factors are static and variations are more likely to result from shocks to the environment. The shocks influence the usage of the equipment. A second forecasting approach using time-series analysis has been demonstrated to be superior for these applications. This paper discusses the integration of the two methods into a unified system. The result is a time-series model whose parameter effects become dynamic in character. An example of the models being used at the Georgia Power Company is presented. It is demonstrated that a time-series model which incorporates end-use stock and usage information is superior—even in short-term forecasting situations—to a similar time-series model which excludes the information.