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The increase of wind penetration into electric power system creates challenges to power grid management due to the variable nature of wind. The information provided by wind power forecasting is thus essential to the strategic deployment of electricity generating resources and can help operators to maintain electrical grid stability and to bid in electricity markets. Much recent research in wind power...
Stream data is considered as one of the main sources of big data. The inherent scarcity of labeled instances and the underlying concept drift have posed significant challenges on stream data classification in practice. A paired ensemble active learning framework is proposed to tackle the challenges. First, an ensemble model consists of two base classifiers is exploited to detect the changes over time,...
Common approaches to addressing renewal and replacement rely on limited analysis of maintenance strategies and on deterministic approaches with finite, best approximation inputs. These over-simplified approaches yield results that greatly overestimate, or in many cases greatly underestimate, uncertainty and risk. Decision makers are often placed in a poor position to defend the annual budget as well...
In the present work, a global optimization method known as the Generalized Geometric Programming (GGP) is used. The technique of convexification used in the present work is different from others presented in earlier works. The presented GGP allows to obtain the global optimum by few transformation applied to the original optimization problem. But for the other convexification technique many constraints...
At The Boeing Company, stock levels for maintenance spares with substantial lead times must be established before fielding new aircraft designs. Initial calculations use mean time between demand estimates developed by the engineering department. After sufficient operating hours, stock levels are recalculated using statistical forecasts of maintenance history. A Bayesian forecasting method was developed...
In recent years there has been growing interest in prediction models for non-conventional energy sources and demand in electrical systems because of the increasing use of renewable energy sources. The prediction interval models proposed in this paper are validated using local load data from a real-life micro grid in Huatacondo, Chile. The micro grid operates with an energy management system (EMS),...
Handling of uncertainty in Model Predictive Control (MPC) has received increasing attention the last decade. The robust open-loop approach often leads to overly conservative solutions, because constraints have to be satisfied for all possible realizations of the stochastic variables, over the entire prediction horizon. In this paper, we present a novel scenario-based approach, where the constraints...
Offshore pipelines are used to transport oil and gas in different areas of the world. The economic importance of offshore pipelines has increased in recent years with the development of oil and gas transportation systems. Hydrocarbons are transported in the pipelines at high temperature and pressure in order to facilitate the flow of oil and to prevent its “solidification”. However, these high temperatures...
High false alarm rates in intensive care units (ICUs) cause desensitization among care providers, thus risking patients' lives. Providing early detection of true and false cardiac arrhythmia alarms can alert hospital personnel and avoid alarm fatigue, so that they can act only on true life-threatening alarms, hence improving efficiency in ICUs. However, suppressing false alarms cannot be an excuse...
Graphs are a fundamental model to describe complex statistical relationships over many scientific domains. In this context, graphs are commonly used to investigate relational phenomena which are not directly observable. Our work formulates a Latent Network Inference problem and develops inference methods in a common context for scientific applications where there is an absence of ground truth.
Traditional online learning algorithms are designed for vector data only, which assume that the labels of all the training examples are provided. In this paper, we study graph classification where only limited nodes are chosen for labelling by selective sampling. Particularly, we first adapt a spectral-based graph regularization technique to derive a novel online learning linear algorithm which can...
In this paper, an adaptive neuro-fuzzy inference system-based grey time-varying sliding mode control (TVSMC) is proposed, and applied for power conditioning systems. The proposed methodology combines the merits of TVSMC, grey prediction (GP), and adaptive neuro-fuzzy inference system (ANFIS). Compared with classic sliding mode control (SMC), the TVSMC can shorten the reaching phase and ensure the...
We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation...
Many industries are applying various methods for optimizing energy use across the manufacturing life cycle. These methods are either physics-based or data-driven. Manufacturing systems generate a vast amount of data from operations and in simulations. Advances in data collection systems and data analytics (DA) tools have enabled the development of predictive analytics for energy prediction. Many of...
Predictive maintenance based on generalized health assessment is a flourishing approach, which can decrease maintenance cost and increase operational availability efficiently. Remaining useful life prediction (RULP) is a kind of health assessment methods, which is fundamental and vital for predictive maintenance. But current predictive maintenance suffers the disconnection between RULP and maintenance...
Demand side management (DSM) programs aim at reducing energy consumption on the demand side, which benefits both consumers and utilities. These programs could also help maintain the critical balance between generation and demand in isolated microgrids. In this case, demand is a treated as a significant uncertainty in the context of dispatching the power resources of a microgrid. Thus, in this paper...
Images captured by digital cameras are generally not perfect as image blurring is usually generated by camera motion through long hand-held exposure. Deblurring filters can be used to improve image quality by removing image blur. Prior to develop a deblurring filter, a simulator for image quality assessment is essential to optimize filter parameters. Although subjective image quality assessment (subjective...
Recently, different algorithms for identification of several uncertain nonlinear models, referred to as Volterra-type models, were introduced in the literature. This work deals with the development of a suitable model-based control algorithm able to cope with the uncertain characterization of those types of models. Particularly, a robust model predictive control (MPC) scheme for multi-input multi-output...
The paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system’s input and output tracking...
In this work we apply a bootstrap method to obtain probabilistic forecasts from past single-valued forecasts offered by a Numerical Weather Prediction model. The potential of the proposed method is assessed with real data from four wind farms in Eastern Canada. The methodology can be extended to other existing point forecasting methods.
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