Forecasting Electrical Load Using a Multi-time-scale Approach

This paper describes the application of a multi-time-scale technique to the modelling and forecasting of short-term electrical load. The multi-time-scale technique is based on adjusting the underlying short sampling period forecast time series with specific target points and possible aggregated demand. This allows not only improvement of the short sampling period forecast, but also focuses on weighting the accuracy of the forecast at certain critical points e.g. the overnight minimum and daily peaks. Various model types may be utilised at the upper level (forecating the aggregated consumption and target points at daily level), including intelligent models such as neural and fuzzy models, but the base model is currently restricted to a linear form. Results for the Irish national electrical grid demonstrate the effectiveness of the technique

Forecasting of Weekly Electricity Consumption Using Neural Networks

Neural networks have been shown to be effective in modelling time series, with applications in the forecasting of electricity consumption. In applying neural networks to weekly electricity consumption data, several issues, such as selection of network architecture, network structure and input structure need to be addressed. This paper addresses these issues in relation to the current application and also demonstrates that considerable value is to be gained from incorporating the lessons learned from linear time series modelling into the current nonlinear analysis. Results for national Irish weekly electricity data demonstrate the potential improvements which can be obtained using the neural network approach

Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks

This paper examines the application of artificial neural networks (ANNs) to the modelling and forecasting of electricity demand experienced by an electricity supplier. The data used in the application examples relates to the national electricity demand in the Republic of Ireland, generously supplied by the Electricity Supply Board (ESB). The paper focusses on three different time scales of interest to power boards: yearly (up to fifteen years in advance), weekly (up to three years in advance) and hourly (up to 24 h ahead). Electricity demand exhibits considerably different characteristics on these different time scales, both in terms of the underlying autoregressive processes and the causal inputs appropriate to each time scale. Where possible, the ANN-based models draw on the applications experience gained with linear modelling techniques and in one particular case, manual forecasting methods

Evolution of the electronic structure with size in II-VI semiconductor nanocrystals

In order to provide a quantitatively accurate description of the band gap variation with sizes in various II-VI semiconductor nanocrystals, we make use of the recently reported tight-binding parametrization of the corresponding bulk systems. Using the same tight-binding scheme and parameters, we calculate the electronic structure of II-VI nanocrystals in real space with sizes ranging between 5 and 80 {\AA} in diameter. A comparison with available experimental results from the literature shows an excellent agreement over the entire range of sizes.Comment: 17 pages, 4 figures, accepted in Phys. Rev.

Effect of the Surface on the Electron Quantum Size Levels and Electron g-Factor in Spherical Semiconductor Nanocrystals

The structure of the electron quantum size levels in spherical nanocrystals is studied in the framework of an eight--band effective mass model at zero and weak magnetic fields. The effect of the nanocrystal surface is modeled through the boundary condition imposed on the envelope wave function at the surface. We show that the spin--orbit splitting of the valence band leads to the surface--induced spin--orbit splitting of the excited conduction band states and to the additional surface--induced magnetic moment for electrons in bare nanocrystals. This additional magnetic moment manifests itself in a nonzero surface contribution to the linear Zeeman splitting of all quantum size energy levels including the ground 1S electron state. The fitting of the size dependence of the ground state electron g factor in CdSe nanocrystals has allowed us to determine the appropriate surface parameter of the boundary conditions. The structure of the excited electron states is considered in the limits of weak and strong magnetic fields.Comment: 11 pages, 4 figures, submitted to Phys. Rev.

History of clinical transplantation

How transplantation came to be a clinical discipline can be pieced together by perusing two volumes of reminiscences collected by Paul I. Terasaki in 1991-1992 from many of the persons who were directly involved. One volume was devoted to the discovery of the major histocompatibility complex (MHC), with particular reference to the human leukocyte antigens (HLAs) that are widely used today for tissue matching.1 The other focused on milestones in the development of clinical transplantation.2 All the contributions described in both volumes can be traced back in one way or other to the demonstration in the mid-1940s by Peter Brian Medawar that the rejection of allografts is an immunological phenomenon.3,4 © 2008 Springer New York