An exhortation/sermon given at Dandenong Bible Education Centre on 25 July 2010.
A form of religion
2010 Jul
20th Australasian Universities Power Engineering Conference
5–8 December 2010, University of Canterbury, Christchurch, New Zealand
Shu Fan and Rob J Hyndman
Abstract
Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance.
In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiment with the real data from the power system, as well as the on-site operation by the system operator.
John L Pearcea, Jason Beringera, Neville Nichollsa, Rob J Hyndmanb, Petteri Uotilaa, and Nigel J Tappera
a School of Geography and Environmental Science, Monash University, Melbourne, Australia
b Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
Abstract
The influence of synoptic-scale circulations on air quality is an area of increasing interest to air quality management in regards to future climate change. This study presents an analysis where the dominant synoptic ‘types’ over the region of Melbourne, Australia are determined and linked to regional air quality. First, a self-organising map (SOM) is used to generate a time series of synoptic charts that classify the annual daily circulation affecting Melbourne into 20 different synoptic types. SOM results are then employed within the framework of a generalized additive model (GAM) to identify links between synoptic-scale circulations and observed changes air pollutant concentrations. The GAMs estimate shifts in pollutant concentrations under each synoptic type after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence. Results showed the aggregate impact of synoptic circulations in the models to be quite modest as only 5.1% of the daily variance in O3, 4.7% in PM10, and 7.1% in NO2 were explained by shifts in synoptic circulations. Further analysis of the partial residual plots identified that despite a modest response at the aggregate level, individual synoptic categories had differential effects on air pollutants. In particular, increases of up to 40% in NO2 and PM10 and 30% in O3 occur when a synoptic conditions result in a north-easterly gradient wind over the Melbourne area. Additionally, NO2 and PM10 levels also showed increases of up to 40% when a strong high pressure system was centered directly over the Melbourne area. In sum, the unified approach of SOM and GAM proved to be a complementary suite of tools capable of identifying the entire range synoptic circulation patterns over a particular region and quantifying how they influence local air quality.
Keywords: air pollution, generalized additive models, self-organizing maps, and synoptic meteorology.
Quantifying the influence of local meteorology on air quality using generalized additive modelling
2010 Jul
John L Pearcea, Jason Beringera, Neville Nichollsa, Rob J Hyndmanb and Nigel J Tappera
a School of Geography and Environmental Science, Monash University, Melbourne, Australia
b Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
Abstract
Quantifying the observed relationships between local meteorology and air pollution provides air quality managers with a knowledge base that may prove useful in understanding how climate change may potentially impact air quality. This paper presents the estimated response of ozone (O3), particulate matter ≤ 10 μm (PM10), and nitrogen dioxide (NO2) to individual local meteorological variables in Melbourne, Australia over the period of 1999 to 2006. The relationships have been quantified after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence using the framework of a generalized additive modelling (GAM). The nature of the response of each pollutant to individual meteorological variables is presented using partial residual plots described on a percentage scale as marginal effects. The aggregate impact of local meteorology in the models was found to explain 26.3% of the variance in O3, 21.1% in PM10, and 26.7% in NO2. High temperatures resulted in strongest positive response for all pollutants with a 150% increase above the mean for O3 and PM10 and a 120% for NO2. Other variables, such as boundary layer height, winds, water vapour pressure, radiation, precipitation and mean sea-level pressure, display some importance for one or more of the pollutants, but their impact in the models was less pronounced. Overall, this analysis presents a solid foundation for understanding the importance of local meteorology as a driver of regional air pollution in Melbourne in a framework that can be applied in other regions. Additionally, these results can be used to corroborate findings from studies using numerical air quality models.
Keywords: air pollution, climate change, generalized additive models, and meteorology.
Talk to be given at the International Symposium on Forecasting, San Diego, 20–23 June 2010.
Forecasting age-related changes in breast cancer mortality among white and black US women
2010 May
Farah Yasmeen, Rob J Hyndman and Bircan Erbas
Cancer Epidemiology, to appear
Abstract:
The disparity in breast cancer mortality rates among white and black US women is widening, with higher mortality rates among black women. We apply functional time series models on age-specific breast cancer mortality rates for each group of women, and forecast their mortality curves using exponential smoothing state-space models with damping.
The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the US. Mortality data were obtained from the National Centre for Health Statistics (NCHS) available on the SEER*Stat database. We use annual unadjusted breast cancer mortality rates from 1969 to 2004 in 5-year age groups (45−49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84). Age-specific mortality curves were obtained using nonparametric smoothing methods. The curves are then decomposed using functional principal components and we fit functional time series models with four basis functions for each population separately. The curves from each population are forecast and prediction intervals are calculated.
Twenty-year forecasts indicate an over-all decline in future breast cancer mortality rates for both groups of women. This decline is steeper among white women aged 55–73 and black women aged 60–84. For black women under 55 years of age, the forecast rates are relatively stable indicating no significant change in future breast cancer mortality rates among young black women in the next 20 years.
Keywords: Breast cancer mortality, racial and ethnic disparities, screening, trends, forecasting, functional data analysis
Han Lin Shang and Rob J Hyndman
Mathematics and Computers in Simulation (2010), to appear.
Abstract
We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univariate time series forecasting and functional principal component regression techniques. When data in the most recent year are partially observed, we improve point forecast accuracy using dynamic updating methods. We also introduce a nonparametric approach to construct prediction intervals of updated forecasts, and compare the empirical coverage probability with an existing parametric method. Our approaches are data-driven and computationally fast, and hence they are feasible to be applied in real time high frequency dynamic updating. The methods are demonstrated using monthly sea surface temperatures from 1950 to 2008.
Keywords: Functional time series, Functional principal component analysis, Ordinary least squares, Penalized least squares, Ridge regression, Sea surface temperatures, Seasonal time series.
Han Lin Shang, Rob J Hyndman and Heather Booth
Abstract:
Using the age– and sex-specific data of 14 developed countries, we compare the short– to medium-term accuracy of ten principal component methods for forecasting mortality rates andlife expectancy. These ten methods include the Lee-Carter method and many of its variants and extensions. For forecasting mortality rates, the weighted Hyndman-Ullah method provides the most accurate point forecasts, while the Lee-Miller method gives the best point forecast accuracy of life expectancy. Furthermore, the weighted Hyndman-Ullah method provides the most accurate interval forecasts of mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy of life expectancy.
Keywords: mortality forecasting, life expectancy forecasting, principal component methods, Lee-Carter method, interval forecasts, forecasting time series.
The tourism forecasting competition
2010 Apr
George Athanasopoulos, Rob J Hyndman, Haiyan Song and Doris Wu
International Journal of Forecasting, to appear
Abstract We evaluate the performance of various methods for forecasting tourism demand. The data used include 366 monthly series, 427 quarterly series and 518 yearly series, all supplied to us by tourism bodies or by academics from previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate only on tourism demand data; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as point forecast accuracy; (iv) we observe the effect of temporal aggregation on forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.
Keywords: ARIMA, exponential smoothing, state space model, time varying parameter model, dynamic regression, autoregressive distributed lag model, vector autoregression.
New Bible translations
2010 Apr
In this talk, I review some of the major new English Bible translations that have appeared since 2000, along with three that are planned for the next 12 months. The talk is to be given at the Dandenong Bible Education Centre at 8pm on 7 April 2010.
