Learning in neural networks and stochastic approximation methods with averaging

The problem of adjusting the weights (learning) in multilayer feedforward neural networks (NN) is known to be of a high importance when utilizing NN techniques in various practical applications. The learning procedure is to be performed as fast as possible and in a simple computational fashion, the two requirements which are usually not satisfied practically by the methods developed so far. Moreover, the presence of random inaccuracies are usually not taken into account. In view of these three issues, an alternative stochastic approximation approach discussed in the paper, seems to be very promising.

Making the least active pay: a simulation of rewards and penalties under Demand Side Participation programs

The orthodox approach for incentivising Demand Side Participation (DSP) programs is that utility losses from capital, installation and planning costs should be recovered under financial incentive mechanisms which aim to ensure that utilities have the right incentives to implement DSP activities. The recent national smart metering roll-out in the UK implies that this approach needs to be reassessed since utilities will recover the capital costs associated with DSP technology through bills. This paper introduces a reward and penalty mechanism focusing on residential users. DSP planning costs are recovered through payments from those consumers who do not react to peak signals. Those consumers who do react are rewarded by paying lower bills. Because real-time incentives to residential consumers tend to fail due to the negligible amounts associated with net gains (and losses) or individual users, in the proposed mechanism the regulator determines benchmarks which are matched against responses to signals and caps the level of rewards/penalties to avoid market distortions. The paper presents an overview of existing financial incentive mechanisms for DSP; introduces the reward/penalty mechanism aimed at fostering DSP under the hypothesis of smart metering roll-out; considers the costs faced by utilities for DSP programs; assesses linear rate effects and value changes; introduces compensatory weights for those consumers who have physical or financial impediments; and shows findings based on simulation runs on three discrete levels of elasticity.

The elasticity of demand is about occupancy, not prices: building European demand curves based on occupancy elasticity

The peak congestion of the European grid may create significant impacts on system costs because of the need for higher marginal cost generation, higher cost system balancing and increasing grid reinforcement investment. The use of time of use rates, incentives, real time pricing and other programmes, usually defined as Demand Side Management (DSM), could bring about significant reductions in prices, limit carbon emissions from dirty power plants, and improve the integration of renewable sources of energy. Unlike previous studies on elasticity of residential electricity demand under flat tariffs, the aim of this study is not to investigate the known relatively inelastic relationship between demand and prices. Rather, the aim is to assess how occupancy levels vary in different European countries. This reflects the reality of demand loads, which are predominantly determined by the timing of human activities (e.g. travelling to work, taking children to school) rather than prices. To this end, two types of occupancy elasticity are estimated: baseline occupancy elasticity and peak occupancy elasticity. These represent the intrinsic elasticity associated with human activities of single residential end-users in 15 European countries. This study makes use of occupancy time-series data from the Harmonised European Time Use Survey database to build European occupancy curves; identify peak occupancy periods; draw time use demand curves for video and TV watching activity; and estimate national occupancy elasticity levels of single-occupant households. Findings on occupancy elasticities provide an indication of possible DSM strategies based on occupancy levels and not prices.

Demand response experience in Europe: policies, programmes and implementation

Over the last few years, load growth, increases in intermittent generation, declining technology costs and increasing recognition of the importance of customer behaviour in energy markets have brought about a change in the focus of Demand Response (DR) in Europe. The long standing programmes involving large industries, through interruptible tariffs and time of day pricing, have been increasingly complemented by programmes aimed at commercial and residential customer groups. Developments in DR vary substantially across Europe reflecting national conditions and triggered by different sets of policies, programmes and implementation schemes. This paper examines experiences within European countries as well as at European Union (EU) level, with the aim of understanding which factors have facilitated or impeded advances in DR. It describes initiatives, studies and policies of various European countries, with in-depth case studies of the UK, Italy and Spain. It is concluded that while business programmes, technical and economic potentials vary across Europe, there are common reasons as to why coordinated DR policies have been slow to emerge. This is because of the limited knowledge on DR energy saving capacities; high cost estimates for DR technologies and infrastructures; and policies focused on creating the conditions for liberalising the EU energy markets.

Discrete demand side control performance under dynamic building simulation: a heat pump application

This study presents the findings of applying a Discrete Demand Side Control (DDSC) approach to the space heating of two case study buildings. High and low tolerance scenarios are implemented on the space heating controller to assess the impact of DDSC upon buildings with different thermal capacitances, light-weight and heavy-weight construction. Space heating is provided by an electric heat pump powered from a wind turbine, with a back-up electrical network connection in the event of insufficient wind being available when a demand occurs. Findings highlight that thermal comfort is maintained within an acceptable range while the DDSC controller maintains the demand/supply balance. Whilst it is noted that energy demand increases slightly, as this is mostly supplied from the wind turbine, this is of little significance and hence a reduction in operating costs and carbon emissions is still attained.

Large-scale length and time scales for use with stochastic convective parameterization

Many numerical models for weather prediction and climate studies are run at resolutions that are too coarse to resolve convection explicitly, but too fine to justify the local equilibrium assumed by conventional convective parameterizations. The Plant-Craig (PC) stochastic convective parameterization scheme, developed in this paper, solves this problem by removing the assumption that a given grid-scale situation must always produce the same sub-grid-scale convective response. Instead, for each timestep and gridpoint, one of the many possible convective responses consistent with the large-scale situation is randomly selected. The scheme requires as input the large-scale state as opposed to the instantaneous grid-scale state, but must nonetheless be able to account for genuine variations in the largescale situation. Here we investigate the behaviour of the PC scheme in three-dimensional simulations of radiative-convective equilibrium, demonstrating in particular that the necessary space-time averaging required to produce a good representation of the input large-scale state is not in conflict with the requirement to capture large-scale variations. The resulting equilibrium profiles agree well with those obtained from established deterministic schemes, and with corresponding cloud-resolving model simulations. Unlike the conventional schemes the statistics for mass flux and rainfall variability from the PC scheme also agree well with relevant theory and vary appropriately with spatial scale. The scheme is further shown to adapt automatically to changes in grid length and in forcing strength.

Integrating spatial dependence into stochastic frontier analysis

An approach to incorporate spatial dependence into stochastic frontier analysis is developed and applied to a sample of 215 dairy farms in England and Wales. A number of alternative specifications for the spatial weight matrix are used to analyse the effect of these on the estimation of spatial dependence. Estimation is conducted using a Bayesian approach and results indicate that spatial dependence is present when explaining technical inefficiency.

Working Group 1: Future developments in deterministic and stochastic parametrizations

Our group considered the desirability of including representations of uncertainty in the development of parameterizations. (By ‘uncertainty’ here we mean the deviation of sub-grid scale fluxes or tendencies in any given model grid box from truth.) We unanimously agreed that the ECWMF should attempt to provide a more physical basis for uncertainty estimates than the very effective but ad hoc methods being used at present. Our discussions identified several issues that will arise.

Stochastic parameterization: uncertainties from convection

In 2005, the ECMWF held a workshop on stochastic parameterisation, at which the convection was seen as being a key issue. That much is clear from the working group reports and particularly the statement from working group 1 that “it is clear that a stochastic convection scheme is desirable”. The present note aims to consider our current status in comparison with some of the issues raised and hopes expressed in that working group report.