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Forecasting Of The Wind Speed Under Uncertainty
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Book Synopsis Forecasting of the wind speed under uncertainty by : Muhammad Aslam
Download or read book Forecasting of the wind speed under uncertainty written by Muhammad Aslam and published by Infinite Study. This book was released on with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, the semi-average method under neutrosophic statistics is introduced. The trend regression line for the semi-average method is given in the presence of Neutrosophy in the data. The application of the semi-average method under indeterminacy is given with the help of wind speed data. The efficiency of the semi-average method under the neutrosophic statistics is discussed over the semi-average method under classical statistics. From the analysis, it is concluded that the proposed method is effective, informative, and flexible for the forecasting of wind speed.
Book Synopsis Completing the Forecast by : National Research Council
Download or read book Completing the Forecast written by National Research Council and published by National Academies Press. This book was released on 2006-10-09 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.
Book Synopsis Statistical Postprocessing of Ensemble Forecasts by : Stéphane Vannitsem
Download or read book Statistical Postprocessing of Ensemble Forecasts written by Stéphane Vannitsem and published by Elsevier. This book was released on 2018-05-17 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place Provides real-world examples of methods used to formulate forecasts Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner
Book Synopsis Uncertainties in Numerical Weather Prediction by : Haraldur Olafsson
Download or read book Uncertainties in Numerical Weather Prediction written by Haraldur Olafsson and published by Elsevier. This book was released on 2020-11-25 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with surface-atmosphere exchange, orographic flows and processes in the atmospheric boundary layer. Through a better understanding of the uncertainties to watch for, readers will be able to produce more precise and accurate forecasts. This is an essential work for anyone who wants to improve the accuracy of weather and climate forecasting and interested parties developing tools to enhance the quality of such forecasts. Provides a comprehensive overview of the state of numerical weather prediction at spatial scales, from hundreds of meters, to thousands of kilometers Focuses on short-term 1-15 day atmospheric predictions, with some coverage appropriate for longer-term forecasts Includes references to climate prediction models to allow applications of these techniques for climate simulations
Book Synopsis Introduction to Neutrosophic Statistics by : Florentin Smarandache
Download or read book Introduction to Neutrosophic Statistics written by Florentin Smarandache and published by Infinite Study. This book was released on 2014 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neutrosophic Statistics means statistical analysis of population or sample that has indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data. For example, the population or sample size might not be exactly determinate because of some individuals that partially belong to the population or sample, and partially they do not belong, or individuals whose appurtenance is completely unknown. Also, there are population or sample individuals whose data could be indeterminate. In this book, we develop the 1995 notion of neutrosophic statistics. We present various practical examples. It is possible to define the neutrosophic statistics in many ways, because there are various types of indeterminacies, depending on the problem to solve.
Book Synopsis Physical Approach to Short-Term Wind Power Prediction by : Matthias Lange
Download or read book Physical Approach to Short-Term Wind Power Prediction written by Matthias Lange and published by Springer Science & Business Media. This book was released on 2006-01-16 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: The effective integration of wind energy into the overall electricity supply is a technical and economical challenge because the availability of wind power is determined by fluctuating meteorological conditions. This book offers an approach to the ultimate goal of the short-term prediction of the power output of winds farms. Starting from basic aspects of atmospheric fluid dynamics, the authors discuss the structure of winds fields, the available forecast systems and the handling of the intrinsic, weather-dependent uncertainties in the regional prediction of the power generated by wind turbines. This book addresses scientists and engineers working in wind energy related R and D and industry, as well as graduate students and nonspecialists researchers in the fields of atmospheric physics and meteorology.
Book Synopsis A Short-term Ensemble Wind-speed Forecasting System for Wind Power Applications by : Justin J. Traiteur
Download or read book A Short-term Ensemble Wind-speed Forecasting System for Wind Power Applications written by Justin J. Traiteur and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate short-term wind speed forecasts for utility-scale wind farms will be crucial for the U.S. Department of Energy0́9s (DOE) goal of providing 20% of total power from wind by 2030. For typical pitch-controlled wind turbines, power output varies as the cube of wind speed over a significant portion of the power output curve. Therefore, small improvements in wind-speed forecasts would constitute much larger improvements in wind power forecasts. In addition, communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate forecasting system is developed. This system uses a 21-member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of 1-hour wind speed forecasts and their level of uncertainty.
Book Synopsis Wind Power Ensemble Forecasting by : André Gensler
Download or read book Wind Power Ensemble Forecasting written by André Gensler and published by kassel university press GmbH. This book was released on 2019-01-16 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power forecasting and proposes a novel scheme for the situation-dependent aggregation of forecasting models. For performance measures, error scores for deterministic as well as probabilistic forecasts are compared, and their characteristics are shown in detail. For the evaluation of deterministic forecasts, a categorization by basic error measure and normalization technique is introduced that simplifies the process of choosing an appropriate error measure for certain forecasting tasks. Furthermore, a scheme for the common evaluation of different forms of probabilistic forecasts is proposed. Based on the analysis of the error scores, a novel hierarchical aggregation technique for both deterministic and probabilistic forecasting models is proposed that dynamically weights individual forecasts using multiple weighting factors such as weather situation and lead time dependent weighting. In the experimental evaluation it is shown that the forecasting quality of the proposed technique is able to outperform other state of the art forecasting models and ensembles.
Book Synopsis Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint by :
Download or read book Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint written by and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year;(ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted tocharacterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.
Book Synopsis Unit Commitment with Wind Power Generation by :
Download or read book Unit Commitment with Wind Power Generation written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.
Book Synopsis Deep Neural Networks in a Mathematical Framework by : Anthony L. Caterini
Download or read book Deep Neural Networks in a Mathematical Framework written by Anthony L. Caterini and published by Springer. This book was released on 2018-03-22 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.
Book Synopsis Wind Forecasting in Railway Engineering by : Hui Liu
Download or read book Wind Forecasting in Railway Engineering written by Hui Liu and published by Elsevier. This book was released on 2021-06-17 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wind Forecasting in Railway Engineering presents core and leading-edge technologies in wind forecasting for railway engineering. The title brings together wind speed forecasting and railway wind engineering, offering solutions from both fields. Key technologies are presented, along with theories, modeling steps and comparative analyses of forecasting technologies. Each chapter presents case studies and applications, including typical applications and key issues, analysis of wind field characteristics, optimization methods for the placement of a wind anemometer, single-point time series along railways, deep learning algorithms on single-point wind forecasting, reinforcement learning algorithms, ensemble single-point wind forecasting methods, spatial wind, and data-driven spatial-temporal wind forecasting algorithms. This important book offers practical solutions for railway safety, by bringing together the latest technologies in wind speed forecasting and railway wind engineering into a single volume. Presents the core technologies and most advanced developments in wind forecasting for railway engineering Gives case studies and experimental designs, demonstrating real-world applications Introduces cutting-edge deep learning and reinforcement learning methods Combines the latest thinking from wind engineering and railway engineering Offers a complete solution to wind forecasting in railway engineering for the safety of running trains
Book Synopsis Wind Speed Forecasting for Power System Operation by : Xinxin Zhu
Download or read book Wind Speed Forecasting for Power System Operation written by Xinxin Zhu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system operation in terms of the efficiency of the system. The goal of this dissertation is to develop advanced statistical wind speed predictive models to reduce the uncertainties in wind, especially the short-term future wind speed. Moreover, a criterion is proposed to evaluate the performance of models. Cost reduction in power system operation, as proposed, is more realistic than prevalent criteria, such as, root mean square error (RMSE) and absolute mean error (MAE). Two advanced space-time statistical models are introduced for short-term wind speed forecasting. One is a modified regime-switching, space-time wind speed fore-casting model, which allows the forecast regimes to vary according to the dominant wind direction and seasons. Thus, it avoids a subjective choice of regimes. The other one is a novel model that incorporates a new variable, geostrophic wind, which has strong influence on the surface wind, into one of the advanced space-time statistical forecasting models. This model is motivated by the lack of improvement in forecast accuracy when using air pressure and temperature directly. Using geostrophic wind in the model is not only critical, it also has a meaningful geophysical interpretation. The importance of model evaluation is emphasized in the dissertation as well. Rather than using RMSE or MAE, the performance of both wind forecasting models mentioned above are assessed by economic benefits with real wind farm data from Pacific Northwest of the U.S and West Texas. Wind forecasts are incorporated into power system economic dispatch models, and the power system operation cost is used as a loss measure for the performance of the forecasting models. From another perspective, the new criterion leads to cost-effective scheduling of system-wide wind generation with potential economic benefits arising from the system-wide generation of cost savings and ancillary services cost savings. As an illustration, the integrated forecasts and economic dispatch framework are applied to the Electric Reliability Council of Texas (ERCOT) equivalent 24- bus system. Compared with persistence and autoregressive models, the first model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars. For the second model, numerical simulations suggest that the overall generation cost can be reduced by up to 6.6% using look-ahead dispatch coupled with spatio-temporal wind forecast as compared with dispatch with persistent wind forecast model. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151248
Book Synopsis Intra-hour Wind Power Variability Assessment Using the Conditional Range Metric by : Thekla Boutsika
Download or read book Intra-hour Wind Power Variability Assessment Using the Conditional Range Metric written by Thekla Boutsika and published by . This book was released on 2013 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research presented herein concentrates on the quantification, assessment and forecasting of intra-hour wind power variability. Wind power is intrinsically variable and, due to the increase in wind power penetration levels, the level of intra-hour wind power variability is expected to increase as well. Existing metrics used in wind integration studies fail to efficiently capture intra-hour wind power variation. As a result, this can lead to an underestimation of intra-hour wind power variability with adverse effects on power systems, especially their reliability and economics. One major research focus in this dissertation is to develop a novel variability metric which can effectively quantify intra-hour wind power variability. The proposed metric, termed conditional range metric (CRM), quantifies wind power variability using the range of wind power output over a time period. The metric is termed conditional because the range of wind power output is conditioned on the time interval length k and on the wind power average production l[subscript j] over the given time interval. Using statistical analysis and optimization approaches, a computational algorithm to obtain a unique p[superscript th] quantile of the conditional range metric is given, turning the proposed conditional range metric into a probabilistic intra-hour wind power variability metric. The probabilistic conditional range metric CRM[subscript k,l subscript j,p] assists power system operators and wind farm owners in decision making under uncertainty, since decisions involving wind power variability can be made based on the willingness to accept a certain level of risk [alpha] = 1 - p. An extensive performance analysis of the conditional range metric on real-world wind power and wind speed data reveals how certain variables affect intra-hour wind power variability. Wind power variability over a time frame is found to increase with increasing time frame size and decreasing wind farm size, and is highest at mid production wind power levels. Moreover, wind turbines connected through converters to the grid exhibit lower wind power variability compared to same size simple induction generators, while wind power variability is also found to decrease slightly with increasing wind turbine size. These results can lead to improvements in existing or definitions of new wind power management techniques. Moreover, the comparison of the conditional range metric to the commonly used step-changes statistics reveals that, on average, the conditional range metric can accommodate intra-hour wind power variations for an additional 15% of hours within a given year, significantly benefiting power system reliability. The other major research focus in this dissertation is on providing intrahour wind power variability forecasts. Wind power variability forecasts use pth CRM quantiles estimates to construct probabilistic intervals within which future wind power output will lie, conditioned on the forecasted average wind power production. One static and two time-adaptive methods are used to obtain p[superscript th] CRM quantiles estimates. All methods produce quantile estimates of acceptable reliability, with average expected deviations from nominal proportions close to 1%. Wind power variability forecasts can serve as joint-chance constraints in stochastic optimization problems, which opens the door to numerous applications of the conditional range metric. A practical example application uses the conditional range metric to estimate the size of an energy storage system (ESS). Using a probabilistic forecast of wind power hourly averages and historical data on intra-hour wind power variability, the proposed methodology estimates the size of an ESS which minimizes deviations from the forecasted hourly average. The methodology is evaluated using real-world wind power data. When the estimated ESS capacities are compared to the ESS capacities obtained from the actual data, they exhibit coverage rates which are very close to the nominal ones, with an average absolute deviation less than 1.5%.
Book Synopsis Extreme Weather Forecasting by : Marina Astitha
Download or read book Extreme Weather Forecasting written by Marina Astitha and published by Elsevier. This book was released on 2022-10-11 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extreme Weather Forecasting reviews current knowledge about extreme weather events, including key elements and less well-known variables to accurately forecast them. The book covers multiple temporal scales as well as components of current weather forecasting systems. Sections cover case studies on successful forecasting as well as the impacts of extreme weather predictability, presenting a comprehensive and model agnostic review of best practices for atmospheric scientists and others who utilize extreme weather forecasts. Reviews recent developments in numerical prediction for better forecasting of extreme weather events Covers causes and mechanisms of high impact extreme events and how to account for these variables when forecasting Includes numerous case studies on successful forecasting, outlining why they worked
Book Synopsis Neutrosophic Probability, Set, And Logic (first version) by : FLORENTIN SMARANDACHE
Download or read book Neutrosophic Probability, Set, And Logic (first version) written by FLORENTIN SMARANDACHE and published by Infinite Study. This book was released on with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project is a part of a National Science Foundation interdisciplinary project proposal. Starting from a new viewpoint in philosophy, the neutrosophy, one extends the classical "probability theory", "fuzzy set" and "fuzzy logic" to , and respectively.
Book Synopsis Methods to Improve High-resolution Forecasts of Surface Wind Speed in the Northeast U.s by : Maria E Frediani
Download or read book Methods to Improve High-resolution Forecasts of Surface Wind Speed in the Northeast U.s written by Maria E Frediani and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Skillful wind speed forecasts in the Northeast U.S. allows for improved preparedness when potentially severe storms advance toward the region. Power outages are frequently caused by uprooted and broken trees and branches, which in turn are provoked by gusts and sustained-high wind speeds. Combining wind forecasts with outage prediction models allow utilities to confidently recruit and allocate crews near vulnerable areas, so power is restored fast and efficiently. The Outage Prediction Model at the University of Connecticut (UConn-OPM) guides emergency response of electric utilities in the Northeast providing outage predictions over their service territory with up to three-day lead time. The UConn-OPM operates with inputs from a Numerical Weather Prediction (NWP) model that provides forecasts for upcoming storms throughout the year. The system performance is determined by the weather forecast skill in simulating storms of varying scales, and in resolving small- scale features driving localized gusts. This thesis encompasses an analysis of NWP wind-speed error sources, a novel methodology to estimate forecast probabilities, and an investigation of error propagation from NWP simulations onto the UConn-OPM outage predictions. First, NWP simulations are evaluated along with an investigation of sources of wind-speed errors, such as terrain, season, and flow patterns. Wind-speed biases depend strongly on flow patterns and the NWP performance is influenced by atmospheric stability. Results motivate the development of a new technique to derive probabilistic forecasts from deterministic NWP model outputs. The object-based analog (Obj-An) forecast technique devised in this study uses spatial features to identify similar storms in the past and previous forecast errors to derive an ensemble representation of the deterministic forecasting uncertainty. The forecast probabilities obtained with the Obj-An technique are based on an algorithm capable of distinguishing flow and spatial patterns through image processing techniques. Finally, the effect of NWP wind simulation errors on power outage predictions is assessed through wind gust simulations using different Planetary Boundary Layer parameterizations and an off-line gust parameterization. The forecasts from the various parameterizations are used in the UConn-OPM to predict outages, through which we evaluate the error propagation from wind and gust simulations to outage predictions.
