Comparison Of Demand Pattern Calibration In Water Distribution Networks With Geographic And Non-Geographic Parameterization

Demands are one of the most uncertain parameters in a water distribution network model. A good calibration of the model demands leads to better solutions when using the model for any purpose. A demand pattern calibration methodology that uses a priori information has been developed for calibrating the behaviour of demand groups. Generally, the behaviours of demands in cities are mixed all over the network, contrary to smaller villages where demands are clearly sectorised in residential neighbourhoods, commercial zones and industrial sectors. Demand pattern calibration has a final use for leakage detection and isolation. Detecting a leakage in a pattern that covers nodes spread all over the network makes the isolation unfeasible. Besides, demands in the same zone may be more similar due to the common pressure of the area rather than for the type of contract. For this reason, the demand pattern calibration methodology is applied to a real network with synthetic non-geographic demands for calibrating geographic demand patterns. The results are compared with a previous work where the calibrated patterns were also non-geographic.

Optimized Water Demand Management Through Intelligent Sensing And Analytics: The WISDOM Approach

New business and technology platforms are required to sustainably manage urban water resources [1,2]. However, any proposed solutions must be cognisant of security, privacy and other factors that may inhibit adoption and hence impact. The FP7 WISDOM project (funded by the European Commission - GA 619795) aims to achieve a step change in water and energy savings via the integration of innovative Information and Communication Technologies (ICT) frameworks to optimize water distribution networks and to enable change in consumer behavior through innovative demand management and adaptive pricing schemes [1,2,3]. The WISDOM concept centres on the integration of water distribution, sensor monitoring and communication systems coupled with semantic modelling (using ontologies, potentially connected to BIM, to serve as intelligent linkages throughout the entire framework) and control capabilities to provide for near real-time management of urban water resources. Fundamental to this framework are the needs and operational requirements of users and stakeholders at domestic, corporate and city levels and this requires the interoperability of a number of demand and operational models, fed with data from diverse sources such as sensor networks and crowsourced information. This has implications regarding the provenance and trustworthiness of such data and how it can be used in not only the understanding of system and user behaviours, but more importantly in the real-time control of such systems. Adaptive and intelligent analytics will be used to produce decision support systems that will drive the ability to increase the variability of both supply and consumption [3]. This in turn paves the way for adaptive pricing incentives and a greater understanding of the water-energy nexus. This integration is complex and uncertain yet being typical of a cyber-physical system, and its relevance transcends the water resource management domain. The WISDOM framework will be modeled and simulated with initial testing at an experimental facility in France (AQUASIM – a full-scale test-bed facility to study sustainable water management), then deployed and evaluated in in two pilots in Cardiff (UK) and La Spezia (Italy). These demonstrators will evaluate the integrated concept providing insight for wider adoption.

Bullied by Budgets, Pushed by Patrons, Driven by Demand: Libraries and Tantalizing Technologies

Libraries are caught in the middle—between static or shrinking budgets on one hand and ever-expanding user needs on the other. How did we get here, and where do we go from here? This paper will offer two perspectives: Part I will present survey results about changing Library purchasing habits in light of changing formats, access, business models and user demands. Data from a previous survey on this topic will be compared and updated. Pricing trends and possible futures will be discussed. Part II will briefly trace the history of libraries’ roles in scholarly communication and connecting learners with knowledge. From there, we show an example of phasing in a patron-driven / demand-driven and short-term loan e-book program, complete with incorporating these tools in library instruction, research, and portable device loadability for field work.

Expediting New Program Development: Meeting Market Demand and Leveraging Internal Resources

Expediting new program development can help universities meet student and employer demand while gaining an edge over competitors, but coordinating program development and approval requires careful preparation and execution. This report profiles strategies to measure market demand for new programs, choose programs for accelerated development, and leverage internal resources. The report also suggests ways to structure and staff program development to maximize speed and effectiveness.

Demanda por alimentos na Região Metropolitana de Porto Alegre : uma aplicação do Almost Ideal Demand System

Este trabalho utilizou os dados da Pesquisa de Orçamentos Familiares (POF) na Região Metropolitana de Porto Alegre (RMPA), realizada pelo Centro de Estudos e Pesquisas Econômicas (IEPE) da Universidade Federal do Rio Grande do Sul (UFRGS), em 1995, para estimar um sistema de demanda por alimentos. Implementou-se o Almost Ideal Demand System (AIDs), proposto por Deaton e Muellbauer. Com base nos coeficientes estimados, calcularam-se as elasticidadespreço, preço-cruzadas e renda para dez subgrupos de alimentos.

Normality under uncertainty

Consider the demand for a good whose consumption be chosen prior to the resolution of uncertainty regarding income. How do changes in the distribution of income affect the demand for this good? In this paper we show that normality, is sufficient to guarantee that consumption increases of the Radon-Nikodym derivative of the new distribution with respect to the old is non-decreasing in the whole domain. However, if only first order stochastic dominance is assumed more structure must be imposed on preferences to guanantee the validity of the result. Finally a converse of the first result also obtains. If the change in measure is characterized by non-decreasing Radon-Nicodyn derivative, consumption of such a good will always increase if and only if the good is normal.

Estimating the stochastic discount factor without a utility function

Using the Pricing Equation in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) which relies on the fact that its logarithm is the serial-correlation ìcommon featureîin every asset return of the economy. Our estimator is a simple function of asset returns, does not depend on any parametric function representing preferences, is suitable for testing di§erent preference speciÖcations or investigating intertemporal substitution puzzles, and can be a basis to construct an estimator of the risk-free rate. For post-war data, our estimator is close to unity most of the time, yielding an average annual real discount rate of 2.46%. In formal testing, we cannot reject standard preference speciÖcations used in the literature and estimates of the relative risk-aversion coe¢ cient are between 1 and 2, and statistically equal to unity. Using our SDF estimator, we found little signs of the equity-premium puzzle for the U.S.

On the differentiability of the consumer demand function

For strictly quasi concave differentiable utility functions, demand is shown to be differentiable almost everywhere if marginal utilities are pointwise Lipschitzian. For concave utility functions, demand is differentiable almost everywhere in the case of differentiable additively separable utility or in the case of quasi-linear utility.

A stochastic discount factor approach to asset pricing using panel data

Using the Pricing Equation, in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) mimicking portfolio which relies on the fact that its logarithm is the ìcommon featureîin every asset return of the economy. Our estimator is a simple function of asset returns and does not depend on any parametric function representing preferences, making it suitable for testing di§erent preference speciÖcations or investigating intertemporal substitution puzzles.