Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Size of the first spring generation of Helicoverpa punctigera (Wallengren) (Lepidoptera : Noctuidae) and winter rain in central Australia

Serious infestations of Helicoverpa punctigera are experienced yearly in the eastern cropping regions of Australia. Regression analysis was used to determine whether the size of the first generation in spring (G(1)), which is comprised mostly of immigrants from inland Australia, was related to monthly rainfall in inland winter breeding areas. Data from two long series of light-trap catches at Narrabri in New South Wales (NSW) and Turretfield in South Australia (SA) were used in the analyses. The size of G1 at Narrabri in each year was significantly regressed on the amount of rainfall in western Queensland and NSW in May and June. The size of G1 at Turretfield each year was significantly regressed on the amount of rain in May, June and July in western Queensland and NSW and also in the desert of central Western Australia. Low r(2) values of the regressions suggest that rainfall data for more sites, as well as biological and other physical factors, such as temperature, evaporation, and prevailing wind systems, may need to be included to improve forecasts of the potential magnitude of the infestations in coastal cropping regions.

Projecting mammographic screens

Objective-The purpose of mammographic screening is to reduce mortality from breast cancer. This study describes a method for projecting the number of screens to be performed by a mammographic screening programme, and applies this method in the context of New South Wales, Australia. Method-The total number of mammographic screens was projected as the sum of initial screens and re-screens, and is based on projections of the population, rates of new recruitment, rates of attrition within the programme, and the mix of screening intervals. The baseline scenario involved: 70% participation of women aged 50-69 years, 90% return rate for the second and subsequent re-screens, 5% annual screens (95% biennial screens), and a specified population projection. The results were assessed with respect to variations in these assumptions. Results-The projections were strongly influenced by: the rate of screening of the target age group; the proportion of women re-screened annually; and the rates of attrition within the programme. Although demographic change had a notable effect, there was little difference between different population projections. Standard assumptions about attrition within the programme suggest that the current target participation rates in NSW may not be achieved in the long term. Conclusions-A practical model for projecting mammographic screens for populations is described which is capable of forecasting the number of screens under different scenarios. Implications-Projections of mammographic screens provide important information for the planning and financing of equipment and personnel, and for testing the effects of variations in important operational parameters. Re-screening attrition is an important contributor to screening viability.

A 2D numerical model for simulating the physics of fault systems

Simulations provide a powerful means to help gain the understanding of crustal fault system physics required to progress towards the goal of earthquake forecasting. Cellular Automata are efficient enough to probe system dynamics but their simplifications render interpretations questionable. In contrast, sophisticated elasto-dynamic models yield more convincing results but are too computationally demanding to explore phase space. To help bridge this gap, we develop a simple 2D elastodynamic model of parallel fault systems. The model is discretised onto a triangular lattice and faults are specified as split nodes along horizontal rows in the lattice. A simple numerical approach is presented for calculating the forces at medium and split nodes such that general nonlinear frictional constitutive relations can be modeled along faults. Single and multi-fault simulation examples are presented using a nonlinear frictional relation that is slip and slip-rate dependent in order to illustrate the model.