Density forecasting for long-term peak electricity demand
AbstractLong-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long term context, planners must adopt a probabilistic view of potential peak demand levels, therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand. Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. We describe a comprehensive forecasting solution in this paper. First, we use semiparametric additive models to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then we forecast the demand distributions using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks. The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. We evaluate the performance of the methodology by comparing the forecast results with the actual demand of the summer 2007/08.
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Bibliographic InfoPaper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 6/08.
Length: 33 pages
Date of creation: Aug 2008
Date of revision:
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Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Web page: http://www.buseco.monash.edu.au/depts/ebs/
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Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-03-22 (All new papers)
- NEP-ECM-2009-03-22 (Econometrics)
- NEP-FOR-2009-03-22 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Javier Sanjuán & Álvaro González & Jaime Lloret, 2013. "Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment," Energies, MDPI, Open Access Journal, vol. 6(9), pages 4489-4507, August.
- José-Fernán Martínez & Jesús Rodríguez-Molina & Pedro Castillejo & Rubén de Diego, 2013. "Middleware Architectures for the Smart Grid: Survey and Challenges in the Foreseeable Future," Energies, MDPI, Open Access Journal, vol. 6(7), pages 3593-3621, July.
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