Modeling and Short-Term Forecasting of New South Wales Electricity System Load
Bibliographic Data
| ID | 19419164 |
|---|---|
| Authors | Michael Smith (0000-0002-7649-353X, a Econometrics and Business Statistics, University of Sydney , Sydney , NSW 2006 , Australia E-mail), Michael S Smith (0000-0002-7589-2037, The University of Sydney, corresponding author) |
| Year | 2000 |
| Volume | 18 |
| Issue | 4 |
| Pages | 465-478 |
| Publication date | 2000-10-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2000.10524885 |
| OpenAlex | W2083696878 |
| Language | EN |
| Citations received | 1 |
| References cited | 19 |
This article employs Bayesian semiparametric regression methodology to model intraday electricity load data and obtain short-term load forecasts. The role of such forecasts in the New South Wales wholesale electricity market is discussed and the method applied to New South Wales system load data. The semiparametric regression model used identifies daily periodic, weekly periodic, and temperature-sensitive components of load. Each component is decomposed as a linear combination of basis functions, with a nonzero probability mass that the corresponding coefficients are exactly zero. Three possible models for the errors are also considered, including independent, autoregressive, and first-differenced autoregressive models. A moving window of data is used to overcome the slow time-varying nature of the temperature and periodic effects. The entire model is estimated using a Bayesian Markov chain Monte Carlo approach, and forecasts are obtained using a Monte Carlo sample from the joint predictive distribution of future system load. It is demonstrated how accurate temperature forecasts can result in accurate intraday system load forecasts for even quite long forecast horizons
Autoregressive model · Bayesian probability · Econometrics · Electricity · Markov chain Monte Carlo · Monte Carlo method · Statistics · Computer Science · Electric Power System Optimization · Energy Load and Power Forecasting · Engineering · Grey System Theory Applications · Mathematics
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |