The Experts below are selected from a list of 53019 Experts worldwide ranked by ideXlab platform
John V. Ringwood - One of the best experts on this subject based on the ideXlab platform.
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On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
IEEE Transactions on Power Systems, 2010Co-Authors: John V. RingwoodAbstract:Weather information is an important factor in load Forecasting models. Typically, load Forecasting models are constructed and tested using actual Weather readings. However, online operation of load Forecasting models requires the use of Weather Forecasts, with associated Weather Forecast errors. These Weather Forecast errors inevitably lead to a degradation in model performance. This is an important factor in load Forecasting but has not been widely examined in the literature. The main aim of this paper is to present a novel technique for minimizing the consequences of this degradation. In addition, a supplementary technique is proposed to model Weather Forecast errors to reflect current accuracy. The proposed technique utilizes a combination of Forecasts from several load Forecasting models (sub-models). The parameter estimation may thus be split into two parts: sub-model and combination parameter estimation. It is shown that the lowest PMSE corresponds to training the sub-models with actual Weather but training the combiner with Forecast Weather.
Frank S. Marzano - One of the best experts on this subject based on the ideXlab platform.
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Validating Weather-Forecast-driven propagation models at millimeter waves using multisource ground-based radiometric data
2019 13th European Conference on Antennas and Propagation (EuCAP), 2019Co-Authors: Luca Milani, Marianna Biscarini, Saverio Di Fabio, Klaide De Sanctis, Mario Montopoli, Kevin M. Magde, George A. Brost, Frank S. MarzanoAbstract:In this work, several sources are used to characterize, in both deterministic and statistical ways, the atmospheric propagation channel in terms of brightness temperature and path attenuation at high frequency bands (such as K- Ka-, V- and W-band). We have used two different models: a Weather-Forecast-driven 3-dimensional radiative transfer model (RTM) and a stochastic 1-dimensional model (SNEM) with synthetic clouds dataset provided as inputs. We have compared the outputs of such radiative transfer simulations with actual measurements of two co-located microwave radiometers: a humidity and temperature profiler and a Sun-tracking radiometer. The comparisons show satisfactory results and a good agreement among all sources, with some small inaccuracies to be investigated in future works. RTM successfully reproduced correlations between brightness temperature and path attenuation at several frequency bands, confirming the advantage of using Weather Forecast models combined with physically-based radiative transfer models. Also, the SNEM model showed to be able to reproduce the atmospheric channel but a proper fine tuning is needed to better represent the climatological conditions of the area of interest.
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Assessment and Uncertainty Estimation of Weather-Forecast Driven Data Transfer for Space Exploration at Ka- and $X$ -Band
IEEE Transactions on Antennas and Propagation, 2019Co-Authors: Marianna Biscarini, Luca Milani, Saverio Di Fabio, Klaide De Sanctis, Mario Montopoli, Frank S. MarzanoAbstract:This paper aims at improving and assessing a previously developed technique to predict data volume transfer during deep-space satellite communication links at Ka- and X -band (where the Earth's atmosphere affects propagating signals). The proposed technique exploits a Weather Forecast (WF) model to predict the atmospheric state and a radiative transfer model to convert the atmospheric state into radiopropagation variables. The latter are used to design the link budget and to maximize transferred data-volume. This WF-based technique exploits the atmospheric attenuation as a random variable related to the statistic of the transmission error rate that drives the received data-volume and its uncertainty. The WF-based technique is evaluated for the test case of the BepiColombo mission to Mercury from ESA (European space agency) considering Cebreros and Malargue receiving ground-stations. Tuning and verification of the adopted models were accomplished exploiting ground-based meteorological measurements (Weather stations, radiosoundings, and microwave radiometer) and simulating four years of data transmission. Results, in terms of yearly received data-volume and its uncertainty, highlight the advantages of short-term WF-based atmospheric statistics in opposition to the commonly used long-term climatological statistics. These advantages are evaluated at both Ka- and X -band. The use of aggregated statistics derived from WF data is demonstrated as a reliable possibility of bypassing the lack of meteorological measurements.
Alexander Klein - One of the best experts on this subject based on the ideXlab platform.
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Using fast-time simulation to assess Weather Forecast accuracy requirements for air traffic flow management
2017 Winter Simulation Conference (WSC), 2017Co-Authors: Alexander KleinAbstract:We present the concept and initial results of using fast-time air traffic simulation modeling to assess requirements for convective Weather Forecast accuracy from a Traffic Flow Management (TFM) standpoint. For strategic TFM applications with longer lead time (2-8 hours), such requirements can be relaxed compared with tactical (0-1 hours) Weather avoidance applications. A gradual increase in Forecast error does not always cause commensurate gradual changes in operational costs (e.g. delays) associated with a given TFM action (for example, a ground delay program or strategic reroute) which was implemented based on that Forecast. But, while such modest inaccuracies in Forecast may not initially require any adjustments to TFM actions, at some point when the discrepancy between Forecast and actual Weather exceeds a certain threshold, it may prompt a different Weather avoidance strategy to be initiated. This, in turn, may cause a significant increase in operational costs. This paper demonstrates how such thresholds, indicative of Forecast accuracy requirements for TFM, can be determined using parametric Forecast accuracy changes in a series of simulations.
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Weather Forecast accuracy study of impact on airport capacity and estimation of avoidable costs
2009Co-Authors: Alexander Klein, Sadegh KavoussiAbstract:It is well known that inclement Weather is the single biggest factor causing air traffic delays in the U.S. What is less well understood is what share in this overall adverse impact belongs to Weather Forecast accuracy. While several en-route convective Forecast analyses have been conducted, the role of terminal/surface Weather Forecast accuracy has not been sufficiently well quantified. The objective of this research is therefore to estimate avoidable delays and costs that can be attributed to terminal Weather Forecast accuracy. We initially focus on arrival delays and cancellations. The well-established Weather-Impacted Traffic Index (WITI) metric based on actual Weather is used as a delay proxy alongside its counterpart, WITI- FA ("Forecast Accuracy") metric based on Forecast Weather. A nomenclature of various relationships between actual and model-estimated arrival rates is built and arrival rate deficit (difference between scheduled and actually achieved rates) attributable to terminal Weather Forecast accuracy is computed for each case. This allows us to estimate the avoidable portion of arrival delays and cancellations due to terminal Weather Forecast inaccuracy, both overall and by specific Weather factor. We show that our model is reasonably realistic and apply it to estimating the benefit pool for improving terminal Forecast accuracy for OEP35 airports. Total benefits are shown to be at least $330M per year for arrival delays due to terminal Weather Forecast inaccuracy alone.
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Using A Convective Weather Forecast Product to Predict Weather Impact on Air Traffic: Methodology and Comparison with Actual Data
2007 Integrated Communications Navigation and Surveillance Conference, 2007Co-Authors: Alexander Klein, Sadegh Kavoussi, David Hickman, David Simenauer, Mark Phaneuf, Thomas MacphailAbstract:In this paper, we present a new method for quantifying the impact of Forecast convective Weather on the National Airspace System (NAS). Data generated by a convective Forecast product (broad areas, percentage coverage and confidence level) is converted to a format similar to the actual convective Weather data (detailed reports on a NAS-wide fine grid). From this, we build a NAS Weather impact index showing how the Forecast and actual Weather might have impacted the air traffic. The aim of the project is to better understand how the accuracy of the Weather impact Forecast (not just the Weather Forecast in itself) affects NAS operational response strategies. The results are compared for several convective seasons and typical cases are discussed.
Marianna Biscarini - One of the best experts on this subject based on the ideXlab platform.
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Validating Weather-Forecast-driven propagation models at millimeter waves using multisource ground-based radiometric data
2019 13th European Conference on Antennas and Propagation (EuCAP), 2019Co-Authors: Luca Milani, Marianna Biscarini, Saverio Di Fabio, Klaide De Sanctis, Mario Montopoli, Kevin M. Magde, George A. Brost, Frank S. MarzanoAbstract:In this work, several sources are used to characterize, in both deterministic and statistical ways, the atmospheric propagation channel in terms of brightness temperature and path attenuation at high frequency bands (such as K- Ka-, V- and W-band). We have used two different models: a Weather-Forecast-driven 3-dimensional radiative transfer model (RTM) and a stochastic 1-dimensional model (SNEM) with synthetic clouds dataset provided as inputs. We have compared the outputs of such radiative transfer simulations with actual measurements of two co-located microwave radiometers: a humidity and temperature profiler and a Sun-tracking radiometer. The comparisons show satisfactory results and a good agreement among all sources, with some small inaccuracies to be investigated in future works. RTM successfully reproduced correlations between brightness temperature and path attenuation at several frequency bands, confirming the advantage of using Weather Forecast models combined with physically-based radiative transfer models. Also, the SNEM model showed to be able to reproduce the atmospheric channel but a proper fine tuning is needed to better represent the climatological conditions of the area of interest.
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Assessment and Uncertainty Estimation of Weather-Forecast Driven Data Transfer for Space Exploration at Ka- and $X$ -Band
IEEE Transactions on Antennas and Propagation, 2019Co-Authors: Marianna Biscarini, Luca Milani, Saverio Di Fabio, Klaide De Sanctis, Mario Montopoli, Frank S. MarzanoAbstract:This paper aims at improving and assessing a previously developed technique to predict data volume transfer during deep-space satellite communication links at Ka- and X -band (where the Earth's atmosphere affects propagating signals). The proposed technique exploits a Weather Forecast (WF) model to predict the atmospheric state and a radiative transfer model to convert the atmospheric state into radiopropagation variables. The latter are used to design the link budget and to maximize transferred data-volume. This WF-based technique exploits the atmospheric attenuation as a random variable related to the statistic of the transmission error rate that drives the received data-volume and its uncertainty. The WF-based technique is evaluated for the test case of the BepiColombo mission to Mercury from ESA (European space agency) considering Cebreros and Malargue receiving ground-stations. Tuning and verification of the adopted models were accomplished exploiting ground-based meteorological measurements (Weather stations, radiosoundings, and microwave radiometer) and simulating four years of data transmission. Results, in terms of yearly received data-volume and its uncertainty, highlight the advantages of short-term WF-based atmospheric statistics in opposition to the commonly used long-term climatological statistics. These advantages are evaluated at both Ka- and X -band. The use of aggregated statistics derived from WF data is demonstrated as a reliable possibility of bypassing the lack of meteorological measurements.
Mihai Anitescu - One of the best experts on this subject based on the ideXlab platform.
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on line economic optimization of energy systems using Weather Forecast information
Journal of Process Control, 2009Co-Authors: Victor M Zavala, Emil M Constantinescu, Theodore R Krause, Mihai AnitescuAbstract:We establish an on-line optimization framework to exploit Weather Forecast information in the operation of energy systems. We argue that anticipating the Weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating Forecasts generated from a state-of-the-art Weather prediction model. The necessary uncertainty information is extracted from the Weather model using an ensemble approach. The accuracy of the Forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.
