Forecasting commercial electricity sales

An important component of the New England Electric System Companies' (the 'System') total electricity sales is attributable to commercial customers. Commercial growth has recently been strong; moreover the System's peak demand is highly sensitive to commercial load. In a typical month this class represents 33 per cent of total System sales. Accurate short-run forecasts of total kWh sales are important for rate making, budgeting, fuel cause proceedings, and corporate planning. In this study we use a variety of econometric and time-series techniques to produce short-run forecasts of commercial sales for two geographical areas served by two separate retail companies;.     

Combination of forecasts in electricity demand prediction

Accurate demand prediction is of great importance in the electricity supply industry. Electricity cannot be stored, and generating plant must be scheduled well in advance to meet future demand. Up to now, where online information about external conditions is unavailable, time series methods on the historical demand series have been used for short-term demand prediction. These have drawbacks, both in their sensitivity to changing weather conditions and in their poor modelling of the daily/weekly business cycles. To overcome these problems a framework has been constructed whereby forecasts from different prediction methods and different forecasting origins can be selected and combined, solely on the basis of recent forecasting performance, with no a priori assumptions of demand behaviour. This added flexibility in univariate forecasting provides a significant improvement in prediction accuracy.     

Modelling peak electricity demand

This paper estimates a forecasting equation for the hourly peak electricity demand one day in the future. The models incorporate deterministic influences such as holidays, stochastic influences such as average loads by building bivariate models, and exogenous influences such as the weather which is given a careful non-linear formulation. Out-of-sample comparisons are made using an additional year of data.     

Forecasting demand in international markets: The case of correlated time series

In this paper a data analysis tool for analyzing highly correlated time series data is suggested. The main objective is to unify multiple time series into a single series and then apply a univariate method for the purpose of prediction. This method is essentially efficient for analyzing multiple time series with sparse data. Several time series data of relative demand for black and white television receivers in various countries are analyzed and quite accurate predictions are obtained.     

Forecasting with money demand functions: the UK case

Money demand functions have long been known to be frequently subject to structural change. Since their use for optimal monetary policy design is basically a forecasting exercise, it is crucial to analyse the effect of time instability on the quality of their forecasts. We discuss in this paper whether instability of demand for money functions precludes their use for policy experiments, analysing a 1963–84 sample for the UK which has been widely used in the literature. © 1998 John Wiley & Sons, Ltd.     

Forecasting capacity and capacity utilization in the U.S. aerospace industry

The forecasting of capacity and its utilization is particularly relevant in the aerospace industry because of long product delivery lead-times and the forward-pricing system. The objective of this paper is to develop a method for forecasting both the industry's capacity and its capacity utilization so that decision makers who must rely on this information may have policy guidance. The result shows that the aerospace industry's capacity expansion rate is closely tied to its present and recent past state of capacity utilization, and to anticipated changes in output. Output, in turn, can be predicted by using Five Year Defense Plans data and information on the cyclical nature of commercial business. Based on these findings, we were able to build an accurate model for forecasting aerospace industry capacity utilization.     

Forecasting time series with increasing seasonal variation

Four options for modeling and forecasting time series data containing increasing seasonal variation are discussed, including data transformations, double seasonal difference models and two kinds of transfer function-type ARIMA models employing seasonal dummy variables. An explanation is given for the typical ARIMA model identification analysis failing to identify double seasonal difference models for this kind of data. A logical process of selecting one option for a particular case is outlined, focusing on issues of linear versus non-linear increasing seasonal variation, and the level of stochastic versus deterministic behavior in a time series. Example models for the various options are presented for six time series, with point forecast and interval forecast comparisons. Interval forecasts from data-transformation models are found to generally be too wide and sometimes illogical in the dependence of their width on the point forecast level. Suspicion that maximum likelihood estimation of ARIMA models leads to excessive indications of unit roots in seasonal moving-average operators is reported.     

State-space modelling of residential,commercial and peak demands

The potential use of state-space modelling is evaluated through comparison with the existing multivariate ARMA models currently in use at Georgia Power Company for forecasting its residential sales, commercial sales and peak demand.     

Forecasting with more than one model

This paper comprises an editorial review for the Special Issue on Combining Forecasts. It gives a background to the current growth of interest in this topic and speculates upon some of the reasons for this popularity. Some of the main methodological issues in practice are also described.     

旅游需求预测方法的比较分析

需求预测是旅游计划管理的一项重要工作。旅游需求预测对于旅游地规划和建设旅游基础设施,组织产品和提供旅游者服务是一个基础性的前期工作。然而,旅游产品的易逝性和不可贮存之特征,对旅游需求预测提出了更高的要求。本文试图通过总结和评价自20世纪中期以来旅游需求预测的方法,确立一个适合于中国旅游发展的新思路。     

Forecasting quality and information

It is assumed that demand for information that subjectively appears to be relevant for forecasting improves forecasting quality. To study this hypothesis a number of forecasting experiments were conducted. Fifty managers from the housing business, from banking, and from a research institution were asked to forecast interest rates, using a Delphi process. They communicated via a computer system, and, to support their judgements, they had access to a data bank that was stored in the same system. Their communication with the system was automatically recorded. Part of the data collected in these experiments is used to study the existence of a relationship between information activities and forecasting results. A weak positive relationship is found if non-linear functions are tested, where information demand is corrected by those data retrievals that seem to have resulted from an inability to handle the information system. For further research a more general, albeit less informative, Boolean model is suggested.     

Forecasting ski demand: comparing learning curve and varying parameter coefficient approaches

Demand for skiing expanded rapidly in the 1980s, fell quite dramatically at the start of the 1990s as the economy declined but has not subsequently recovered. Two possible explanations are explored. The first is based on perceiving skiing as a new product to most consumers, which reached maximum growth in 1989. Current levels now largely represent ‘repeat buyers’. The alternative approach sees the growth as the result of economic factors, particularly credit conditions. The importance of these factors was not, however, constant, and grew with the changes in the financial system. Thus the recovery had a muted effect. These two approaches are modelled, estimated and the results compared by both residual and ex post forecasting analysis. The paper concludes that the varying coefficient econometric model probably produces the most reliable forecasts. Copyright © 1999 John Wiley & Sons, Ltd.     

Simultaneous equation estimates of electricity demand for the rural south: Revenue projection when prices are administered

Presented are estimates of demand equations and producer revenue projections for rural farm electricity consumers in the USA. Statistical tests include a Box-Cox comparison of functional form, a Koyck-distributed lag, and a contrast of average versus marginal prices. Producer revenue projections take account of the estimated demand equation and non-continuous rate schedules for each of the electricity distributors. Multiple price equilibria result from differences in administered price schedules between sellers. The ‘effective’ price elasticity based upon forecasts which take account of the market circumstances is lower than the simple demand point elasticity but higher than found in some previous studies.     

Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations

This article discusses the use of Bayesian methods for inference and forecasting in dynamic term structure models through integrated nested Laplace approximations (INLA). This method of analytical approximation allows accurate inferences for latent factors, parameters and forecasts in dynamic models with reduced computational cost. In the estimation of dynamic term structure models it also avoids some simplifications in the inference procedures, such as the inefficient two‐step ordinary least squares (OLS) estimation. The results obtained in the estimation of the dynamic Nelson–Siegel model indicate that this method performs more accurate out‐of‐sample forecasts compared to the methods of two‐stage estimation by OLS and also Bayesian estimation methods using Markov chain Monte Carlo (MCMC). These analytical approaches also allow efficient calculation of measures of model selection such as generalized cross‐validation and marginal likelihood, which may be computationally prohibitive in MCMC estimations. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting with preliminary data

This paper examines several methods to forecast revised US trade balance figures by incorporating preliminary data. Two benchmark forecasts are considered: one ignoring the preliminary data and the other applying a combination approach; with the second outperforming the first. Competing models include a bivariate AR error-correction model and a bivariate AR error-correction model with GARCH effects. The forecasts from the latter model outperforms the combination benchmark for the one-step forecast case only. A restricted AR error-correction model with GARCH effects is discovered to provide the best forecasts. © 1997 John Wiley & Sons, Ltd.     

Forecasting economic time series that require a power transformation: Case of state tax receipts

In this study, time series analysis is applied to the problem of forecasting state income tax receipts. The data series is of special interest since it exhibits a strong trend with a high multiplicative seasonal component. An appropriate model is identified by simultaneous estimation of the parameters of the power transformation and the ARMA model using the Schwarz (1978) Bayesian information criterion. The forecasting performance of the time series model obtained from this procedure is compared with alternative time series and regression models. The study illustrates how an information criterion can be employed for identifying time series models that require a power transformation, as exemplified by state tax receipts. It also establishes time series analysis as a viable technique for forecasting state tax receipts.     

A robust neural network filter for electricity demand prediction

This paper is concerned with one-day-ahead hourly predictions of electricity demand for Puget Power, a local electricity utility for the Seattle area. Standard modelling techniques, including neural networks, will fail when the assumptions of the model are violated. It is demonstrated that typical modelling assumptions such as no outliers or level shifts are incorrect for electric power demand time series. A filter which removes or lessens the significance of outliers and level shifts is demonstrated. This filter produces ‘clean data’ which is used as the basis for future robust predictions. The robust predictions are shown to be better than non-robust counterparts on electricity load data. The outliers identified by the filter are shown to correspond with suspicious data. Finally, the estimated level shifts are in agreement with the belief that load growth is taking place year to year.     

Forecasting vehicle holdings and usage with a disaggregate choice model

A case study in which a three-stage choice model of Canadian household vehicle holdings and usage is used to generate short-run forecasts of changes in household vehicle usage and gasoline consumption in response to a range of energy-related policies. The objectives of this case study are to (1) demonstrate the application of disaggregate choice modelling methods to the generation of policy-relevant forecasts of travel behaviour; (2) draw implications from this forecasting exercise concerning the likely impacts of various energy-related policies; and (3) assess some of the strengths and weaknesses of the current state-of-the-art of forecasting with disaggregate choice models, using the presented study as a case in point.     

Forecasting US GNP growth: The role of uncertainty

A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.     

Structural,VAR and BVAR models of exchange rate determination: A comparison of their forecasting performance

This paper compares the out-of-sample forecasting accuracy of a wide class of structural, BVAR and VAR models for major sterling exchange rates over different forecast horizons. As representative structural models we employ a portfolio balance model and a modified uncovered interest parity model, with the latter producing the more accurate forecasts. Proper attention to the long-run properties and the short-run dynamics of structural models can improve on the forecasting performance of the random walk model. The structural model shows substantial improvement in medium-term forecasting accuracy, whereas the BVAR model is the more accurate in the short term. BVAR and VAR models in levels strongly out predict these models formulated in difference form at all forecast horizons.