20,795 research outputs found
Organic Farming in Europe by 2010: Scenarios for the future
How will organic farming in Europe evolve by the year 2010? The answer provides a basis for the development of different policy options and for anticipating the future relative competitiveness of organic and conventional farming. The authors tackle the question using an innovative approach based on scenario analysis, offering the reader a range of scenarios that encompass the main possible evolutions of the organic farming sector.
This book constitutes an innovative and reliable decision-supporting tool for policy makers, farmers and the private sector. Researchers and students operating in the field of agricultural economics will also benefit from the methodological approach adopted for the scenario analysis
Forecasting the demand for privatized transport - What economic regulators should know, and why
Forecasting has long been a challenge, and will remain so for the foreseeable future. But the analytical instruments and data processing capabilities available through the latest technology, and software, should allow much better forecasting than transport ministries, or regulatory agencies typically observe. Privatization brings new needs for demand forecasting. More attention is paid to risk under privatization, than when investments are publicly financed. And regulators must be able to judge traffic studies done by operators, and to learn what strategic behavior influenced these studies. Many governments, and regulators avoid good demand, modeling out of lack of conviction that theory, and models can do better than the"old hands"of the sector. This is dangerous when privatization changes the nature of business. For projects amounting to investments of 100,000-200,000 is not a reason to reject a reasonable modeling effort. And some private forecasting firms are willing to sell guarantees, or insurance with their forecasts, to cover significant gaps between forecasts, and reality.Markets and Market Access,Environmental Economics&Policies,Economic Theory&Research,Decentralization,Banks&Banking Reform,Markets and Market Access,Economic Theory&Research,Banks&Banking Reform,Access to Markets,Environmental Economics&Policies
From supply chains to demand networks. Agents in retailing: the electrical bazaar
A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version
Holistic Measures for Evaluating Prediction Models in Smart Grids
The performance of prediction models is often based on "abstract metrics"
that estimate the model's ability to limit residual errors between the observed
and predicted values. However, meaningful evaluation and selection of
prediction models for end-user domains requires holistic and
application-sensitive performance measures. Inspired by energy consumption
prediction models used in the emerging "big data" domain of Smart Power Grids,
we propose a suite of performance measures to rationally compare models along
the dimensions of scale independence, reliability, volatility and cost. We
include both application independent and dependent measures, the latter
parameterized to allow customization by domain experts to fit their scenario.
While our measures are generalizable to other domains, we offer an empirical
analysis using real energy use data for three Smart Grid applications:
planning, customer education and demand response, which are relevant for energy
sustainability. Our results underscore the value of the proposed measures to
offer a deeper insight into models' behavior and their impact on real
applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on
Knowledge and Data Engineering, 2014. Authors' final version. Copyright
transferred to IEE
NASA Lewis Research Center Futuring Workshop
On October 21 and 22, 1986, the Futures Group ran a two-day Futuring Workshop on the premises of NASA Lewis Research Center. The workshop had four main goals: to acquaint participants with the general history of technology forecasting; to familiarize participants with the range of forecasting methodologies; to acquaint participants with the range of applicability, strengths, and limitations of each method; and to offer participants some hands-on experience by working through both judgmental and quantitative case studies. Among the topics addressed during this workshop were: information sources; judgmental techniques; quantitative techniques; merger of judgment with quantitative measurement; data collection methods; and dealing with uncertainty
Dynamic Energy Management
We present a unified method, based on convex optimization, for managing the
power produced and consumed by a network of devices over time. We start with
the simple setting of optimizing power flows in a static network, and then
proceed to the case of optimizing dynamic power flows, i.e., power flows that
change with time over a horizon. We leverage this to develop a real-time
control strategy, model predictive control, which at each time step solves a
dynamic power flow optimization problem, using forecasts of future quantities
such as demands, capacities, or prices, to choose the current power flow
values. Finally, we consider a useful extension of model predictive control
that explicitly accounts for uncertainty in the forecasts. We mirror our
framework with an object-oriented software implementation, an open-source
Python library for planning and controlling power flows at any scale. We
demonstrate our method with various examples. Appendices give more detail about
the package, and describe some basic but very effective methods for
constructing forecasts from historical data.Comment: 63 pages, 15 figures, accompanying open source librar
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