The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

Profiling management for personalised multimedia delivery on-demand within the AXMEDIS framework

Media content distribution on-demand becomes more complex when performed on a mass scale involving various channels with distinct and dynamic network characteristics, and, deploying a variety of terminal devices offering a wide range of capabilities. It is practically impossible to create and prepackage various static versions of the same content to match all the varying demand parameters of clients for various contexts. In this paper we present a profiling management approach for dynamically personalised media content delivery on-demand integrated with the AXMEDIS Framework. The client profiles comprise the representation of User, Device, Network and Context of content delivery based on MPEG-21:DIA. Although the most challenging proving ground for this personalised content delivery has been the mobile testbed i.e. the distribution to mobile handsets, the framework described here can be deployed for disribution, by the AXMEDIS PnP module, through other channels e.g. satellite, Internet to a range of client terminals e.g. desktops, kiosks, IPtv and other terrminals whose baseline terminal capabilities can be made availabe by the manufacturers as is normal.

A design for business intelligence service in demand driven supply chain management

This paper discusses the problems inherent within traditional supply chain management's forecast and inventory management processes arising when tackling demand driven supply chain. A demand driven supply chain management architecture developed by Orchestr8 Ltd., U.K. is described to demonstrate its advantages over traditional supply chain management. Within this architecture, a metrics reporting system is designed by adopting business intelligence technology that supports users for decision making and planning supply activities over supply chain health.

A parallel convolutional coder including embedded puncturing with application to consumer devices

As consumers demand more functionality) from their electronic devices and manufacturers supply the demand then electrical power and clock requirements tend to increase, however reassessing system architecture can fortunately lead to suitable counter reductions. To maintain low clock rates and therefore reduce electrical power, this paper presents a parallel convolutional coder for the transmit side in many wireless consumer devices. The coder accepts a parallel data input and directly computes punctured convolutional codes without the need for a separate puncturing operation while the coded bits are available at the output of the coder in a parallel fashion. Also as the computation is in parallel then the coder can be clocked at 7 times slower than the conventional shift-register based convolutional coder (using DVB 7/8 rate). The presented coder is directly relevant to the design of modern low-power consumer devices

Power to the people: working-class demand for household power in 1930s Britain

The 1930s witnessed an intense struggle between gas and electricity suppliers for the working class market, where the incumbent utility—gas—was also a reasonably efficient (and cheaper) General Purpose Technology for most domestic uses. Local monopolies for each supplier boosted substitution effects between fuel types—as alternative fuels constituted the only local competition. Using newly-rediscovered returns from a major national household expenditure survey, we employ geographically-determined instrumental variables, more commonly used in the industrial organization literature, to show that gas provided a significant competitor, tempering electricity prices, while electricity demand was also responsive to marketing initiatives.

The demand for a healthy diet: estimating the almost ideal demand system with infrequency of purchase

A Bayesian method of estimating multivariate sample selection models is introduced and applied to the estimation of a demand system for food in the UK to account for censoring arising from infrequency of purchase. We show how it is possible to impose identifying restrictions on the sample selection equations and that, unlike a maximum likelihood framework, the imposition of adding up at both latent and observed levels is straightforward. Our results emphasise the role played by low incomes and socio-economic circumstances in leading to poor diets and also indicate that the presence of children in a household has a negative impact on dietary quality.