829 research outputs found
Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power
In the probabilistic energy forecasting literature, emphasis is mainly placed
on deriving marginal predictive densities for which each random variable is
dealt with individually. Such marginals description is sufficient for power
systems related operational problems if and only if optimal decisions are to be
made for each lead-time and each location independently of each other. However,
many of these operational processes are temporally and spatially coupled, while
uncertainty in photovoltaic (PV) generation is strongly dependent in time and
in space. This issue is addressed here by analysing and capturing
spatio-temporal dependencies in PV generation. Multivariate predictive
distributions are modelled and space-time trajectories describing the potential
evolution of forecast errors through successive lead-times and locations are
generated. Discrimination ability of the relevant scoring rules on performance
assessment of space-time trajectories of PV generation is also studied.
Finally, the advantage of taking into account space-time correlations over
probabilistic and point forecasts is investigated. The empirical investigation
is based on the solar PV dataset of the Global Energy Forecasting Competition
(GEFCom) 2014.Comment: 33 pages, 11 Figure
Polyhedral Predictive Regions For Power System Applications
Despite substantial improvement in the development of forecasting approaches,
conditional and dynamic uncertainty estimates ought to be accommodated in
decision-making in power system operation and market, in order to yield either
cost-optimal decisions in expectation, or decision with probabilistic
guarantees. The representation of uncertainty serves as an interface between
forecasting and decision-making problems, with different approaches handling
various objects and their parameterization as input. Following substantial
developments based on scenario-based stochastic methods, robust and
chance-constrained optimization approaches have gained increasing attention.
These often rely on polyhedra as a representation of the convex envelope of
uncertainty. In the work, we aim to bridge the gap between the probabilistic
forecasting literature and such optimization approaches by generating forecasts
in the form of polyhedra with probabilistic guarantees. For that, we see
polyhedra as parameterized objects under alternative definitions (under
and norms), the parameters of which may be modelled and predicted.
We additionally discuss assessing the predictive skill of such multivariate
probabilistic forecasts. An application and related empirical investigation
results allow us to verify probabilistic calibration and predictive skills of
our polyhedra.Comment: 8 page
Ellipsoidal Prediction Regions for Multivariate Uncertainty Characterization
While substantial advances are observed in probabilistic forecasting for
power system operation and electricity market applications, most approaches are
still developed in a univariate framework. This prevents from informing about
the interdependence structure among locations, lead times and variables of
interest. Such dependencies are key in a large share of operational problems
involving renewable power generation, load and electricity prices for instance.
The few methods that account for dependencies translate to sampling scenarios
based on given marginals and dependence structures. However, for classes of
decision-making problems based on robust, interval chance-constrained
optimization, necessary inputs take the form of polyhedra or ellipsoids.
Consequently, we propose a systematic framework to readily generate and
evaluate ellipsoidal prediction regions, with predefined probability and
minimum volume. A skill score is proposed for quantitative assessment of the
quality of prediction ellipsoids. A set of experiments is used to illustrate
the discrimination ability of the proposed scoring rule for misspecification of
ellipsoidal prediction regions. Application results based on three datasets
with wind, PV power and electricity prices, allow us to assess the skill of the
resulting ellipsoidal prediction regions, in terms of calibration, sharpness
and overall skill.Comment: 8 pages, 7 Figures, Submitted to IEEE Transactions on Power System
Ban On Plastic Bags Usage: Is It A Right Move? An Empirical Study On Consumer Perception And Practice
Penggunaan beg plastik telah menyebabkan manfaat dan kerugian dalam kehidupan kita sehari-hari.
The usage of plastic bag has causes both convenience and inconvenience in our daily lives
Neural Networks: A Diagnostic Tool for Gastric Electrical Uncoupling?
Neural Networks have been successfully employed in different biomedical settings. They have been
useful for feature extractions from images and biomedical data in a variety of diagnostic applications. In this
paper, they are applied as a diagnostic tool for classifying different levels of gastric electrical uncoupling in
controlled acute experiments on dogs. Data was collected from 16 dogs using six bipolar electrodes inserted into
the serosa of the antral wall. Each dog underwent three recordings under different conditions: (1) basal state, (2)
mild surgically-induced uncoupling, and (3) severe surgically-induced uncoupling. For each condition half-hour
recordings were made. The neural network was implemented according to the Learning Vector Quantization
model. This is a supervised learning model of the Kohonen Self-Organizing Maps. Majority of the recordings
collected from the dogs were used for network training. Remaining recordings served as a testing tool to examine
the validity of the training procedure. Approximately 90% of the dogs from the neural network training set were
classified properly. However, only 31% of the dogs not included in the training process were accurately
diagnosed. The poor neural-network based diagnosis of recordings that did not participate in the training process
might have been caused by inappropriate representation of input data. Previous research has suggested
characterizing signals according to certain features of the recorded data. This method, if employed, would reduce
the noise and possibly improve the diagnostic abilities of the neural network
Very Short-term Nonparametric Probabilistic Forecasting of Renewable Energy Generation - with Application to Solar Energy
Cytomegalovirus retinitis mimicking intraocular lymphoma
We present a case of an unusual retinal infiltrate requiring retinal biopsy for definitive diagnosis. A 62-year-old man with treated lymphoma presented with decreased vision in the right eye associated with a white retinal lesion, which extended inferonasally from an edematous disc. Intraocular lymphoma was considered as a diagnosis; thus, the patient was managed with vitrectomy and retinal biopsy. Cytological analysis of the vitreous aspirate could not rule out a lymphoproliferative disorder. The microbial analysis was negative. Histology of the lesion showed extensive necrosis and large cells with prominent nucleoli. To rule out lymphoma, a battery of immunostains was performed and all were negative. However the limited amount of tissue was exhausted in the process. Subsequently, a hematoxylin and eosin (H/E) slide was destained, on which a CMV immunostain was performed. This revealed positivity in the nuclei and intranuclear inclusions within the large atypical cells. A diagnosis of CMV retinitis was made. Retinal biopsy may provide a definitive diagnosis and direct patient care toward intravenous gancyclovir in the case of CMV or toward radiation and chemotherapy for intraocular lymphoma. When faced with a limited amount of tissue, destaining regular H/E slides is a possible avenue to performing additional immunohistochemical studies
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