33,282 research outputs found
Incentive Design for Direct Load Control Programs
We study the problem of optimal incentive design for voluntary participation
of electricity customers in a Direct Load Scheduling (DLS) program, a new form
of Direct Load Control (DLC) based on a three way communication protocol
between customers, embedded controls in flexible appliances, and the central
entity in charge of the program. Participation decisions are made in real-time
on an event-based basis, with every customer that needs to use a flexible
appliance considering whether to join the program given current incentives.
Customers have different interpretations of the level of risk associated with
committing to pass over the control over the consumption schedule of their
devices to an operator, and these risk levels are only privately known. The
operator maximizes his expected profit of operating the DLS program by posting
the right participation incentives for different appliance types, in a publicly
available and dynamically updated table. Customers are then faced with the
dynamic decision making problem of whether to take the incentives and
participate or not. We define an optimization framework to determine the
profit-maximizing incentives for the operator. In doing so, we also investigate
the utility that the operator expects to gain from recruiting different types
of devices. These utilities also provide an upper-bound on the benefits that
can be attained from any type of demand response program.Comment: 51st Annual Allerton Conference on Communication, Control, and
Computing, 201
Global Market Segmentation for Value-Added Agricultural Products
International Relations/Trade,
Cognitive finance: Behavioural strategies of spending, saving, and investing.
Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services
Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings
Buildings are key actors of the electrical gird. As such they have an important role to play in grid
stabilization, especially in a context where renewable energies are mandated to become an increasingly
important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical
demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate
more efficiently. One of the ways to obtain flexibility from building managers and building users is the
introduction of variable energy prices which evolve depending on the expected load and energy generation.
In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of
consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper,
a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random
Forest machine learning algorithm.This paper is part of a project that has received funding
from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No
768614. This paper reflects only the author´s views and
neither the Agency nor the Commission are responsible
for any use that may be made of the information contained
therein
Conformative Filtering for Implicit Feedback Data
Implicit feedback is the simplest form of user feedback that can be used for
item recommendation. It is easy to collect and is domain independent. However,
there is a lack of negative examples. Previous work tackles this problem by
assuming that users are not interested or not as much interested in the
unconsumed items. Those assumptions are often severely violated since
non-consumption can be due to factors like unawareness or lack of resources.
Therefore, non-consumption by a user does not always mean disinterest or
irrelevance. In this paper, we propose a novel method called Conformative
Filtering (CoF) to address the issue. The motivating observation is that if
there is a large group of users who share the same taste and none of them have
consumed an item before, then it is likely that the item is not of interest to
the group. We perform multidimensional clustering on implicit feedback data
using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups
and make recommendations for a user based on her memberships in the groups and
on the past behavior of the groups. Experiments on two real-world datasets from
different domains show that CoF has superior performance compared to several
common baselines
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