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Recent years have been characterized by an increasing energy demand and by growing attention to energy production sustainability. For this reason, the number of plants powered by renewable sources has increased. Simultaneously, the energy sector has to face increasing uncertainty due to market liberalization. These factors has made it necessary for investors to find a proper evaluation method for...
Consider the problem of allocation of spatially correlated gridded data to finer spatial scale, conditionally on covariate information observable in a fine grid. Spatial dependence of the process can be captured with the conditional autoregressive structure, suitable for gridded (areal level) data. Also geostatistical methods, particularly empirical universal kriging, can be used for this purpose...
This paper proposes a Quasi-Monte Carlo (QMC) simulation based multi-objective economic dispatch, which aims to reduce the fuel cost and emission of the grid simultaneously. During the simulation, QMC models the stochastic behaviours of wind speed and distributed loads with low-discrepancy sequences. In comparison with conventional Monte Carlo (MC) simulation, the computational complexity of QMC is...
In this paper Probabilistic Collocation Method (PCM) is introduced to solve a stochastic model representing wind farms in South Australia (SA). The model is based upon historical acquisition of wind source data, and considering the spatial correlation of wind speeds at neighboring wind farms. This correlation is used to reduce the number of uncertain parameters of the model, and then reducing the...
The inclusion and modeling of uncertainty in conventional load flow is required with the enhancement in the penetration of intermittent generation. As a result, the multi-modality is there in output distribution functions. In this paper, a probabilistic load flow method is used with two wind generator models with multimodal loadings and a spline based reconstruction technique is introduced for the...
We are interested in solving the problem of locating a subset of facilities in the case of uncertainties and variations in the system parameters. Dealing with this problem using scenarios based approach needs an important computational effort. The two phases proposed method in this paper combines both exact and heuristic approaches to minimize the maximum regret of the model. We proposed and compared...
The domain model is one of the important components used by adaptive e-learning systems to generate customized courses for the learners. The domain model acts as a data repository that consists of topics, contents, pages or nodes, and navigation links related to the design structure of the represented data. The most important aspect of the domain model is the relations between the course elements...
Transmission expansion planning should be performed according to the load growth to encourage and facilitate competition in the electricity markets, and meet the environmental, political, economic and technical constraints, appropriately. This paper presents a new framework for the stochastic short-term Dynamic Transmission Expansion Planning (DTEP) problem taking into account the uncertainties related...
The aim of the paper is to present a Bayesian Network (BN) for predicting reputation of members of Virtual Learning Communities using direct interactions between them considering the theory of the rewards of behavioral Psychology. Reputation is a key factor of a trust model. A prototype of the proposed BN was implemented in the Moodle Learning Management System.
A nightmarish list of empirically proposed drivers affecting Information System (IS) adoption, and the limitation of measurements focusing their applicability in Latin America (LAT) economies is an issue. This causes uncertainty in the decision making process of which model and proposed drivers should be used to measure Successful Information System Adoption (SISA) in local public organizations, particularly...
In this paper, we address adaptive predictor feedback design for a simplified drilling system in the presence of disturbance and time-delay. The main objective is to stabilize the bottomhole pressure at a critical depth at a desired set-point directly. The stabilization of the dynamic system and the asymptotic tracking are demonstrated by the proposed adaptive control, where the adaptation employs...
The main inconvenience of time-delay systems is that the plant output is delayed with respect to the plant input. So, in order to monitor, control or evaluate its behavior current information is not available. Dead-time compensators, mainly designed to control delayed plants, are aimed to provide undelayed data from the plant. Obviously, there is always an error between the estimated data and the...
This work is concerned with error control in the numerical approximation of magnetic fields. It is shown how linearization and discretization error can be balanced with respect to a desired numerical accuracy, reflecting an uncertainty in the solution. The uncertainty itself is roughly estimated using a gradient based worst-case approach. The different error measures are illustrated using a numerical...
In this work, we highlight the influence of geometrical uncertainties (winding pattern and wire diameter) on the RLC parameters of wound magnetic components. To that end, the finite element method is embedded in a Monte Carlo simulation in order to compute probability distributions of the parameters. An algorithm to randomly generate realistic winding configurations is also proposed.
The finite-difference time-domain method is herein combined with polynomial-chaos expansions for the study of axially-symmetric structures featuring material uncertainties. By exploiting the problem's periodicity, we reduce the high computational burden of fully 3D simulations, and reliably extract the necessary statistical information from a single simulation.
In this paper, we study a pattern-context-aware scheme for stereo pattern analysis.Depth and texture are chosen as two primary factors for the pattern-context- aware computing.We organize these patterns as a context to analyze.A knowledge-based inference system is built with human experience to model the correlation of the context and processing. The process for the pattern analysis could recognize...
Runtime information of deployed software has been used by business and operations units to make informed decisions under the term “analytics”. However, decisions made by software engineers in the course of evolving software have, for the most part, been based on personal belief and gut-feeling. This could be attributed to software development being, for the longest time, viewed as an activity that...
The increasing penetration of renewables and the constraints posed by pan-European market make more and more crucial the need to evaluate the dynamic behaviour of the whole grid and to cope with forecast uncertainties from operational planning to online environment. The FP7 EU project iTesla addresses these needs and encompasses several major objectives, including the definition of a platform architecture,...
The purpose of this research is to develop a highly reliable simulator of hybrid systems, i.e., systems involving both discrete change and continuous evolution. In particular, we aim at rigorous simulation of parametrized hybrid systems, which enables not only the analysis of model's possible behavior but also the design of parameters that realize desired properties. Simulators with interval arithmetic...
Long-term generation investment (LTGI) models have been widely used as a decision-making tool of design of energy policy. Adequate LTGI models with detailed modelling of operations are often computationally intensive. Uncertainty involved in these models poses a great challenge to the uncertainty quantification in power system reliability. This paper presents a Bayesian framework for addressing this...
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