Forecasting Of The Wind Speed Under Uncertainty
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Forecasting Of The Wind Speed Under Uncertainty
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Author : Muhammad Aslam
language : en
Publisher: Infinite Study
Release Date :
Forecasting Of The Wind Speed Under Uncertainty written by Muhammad Aslam and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
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.
A Short Term Ensemble Wind Speed Forecasting System For Wind Power Applications
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Author : Justin J. Traiteur
language : en
Publisher:
Release Date : 2011
A Short Term Ensemble Wind Speed Forecasting System For Wind Power Applications written by Justin J. Traiteur and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
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.
Physical Approach To Short Term Wind Power Prediction
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Author : Matthias Lange
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-01-16
Physical Approach To Short Term Wind Power Prediction written by Matthias Lange and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-16 with Technology & Engineering categories.
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.
Wind Power Ensemble Forecasting
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Author : André Gensler
language : en
Publisher: kassel university press GmbH
Release Date : 2019-01-16
Wind Power Ensemble Forecasting written by André Gensler and has been published by kassel university press GmbH this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-16 with Weights and measures categories.
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.
Unit Commitment With Wind Power Generation
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Author :
language : en
Publisher:
Release Date : 2009
Unit Commitment With Wind Power Generation written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
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.
Uncertainties In Numerical Weather Prediction
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Author : Haraldur Olafsson
language : en
Publisher: Elsevier
Release Date : 2020-11-25
Uncertainties In Numerical Weather Prediction written by Haraldur Olafsson and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
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
Intra Hour Wind Power Variability Assessment Using The Conditional Range Metric
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Author : Thekla Boutsika
language : en
Publisher:
Release Date : 2013
Intra Hour Wind Power Variability Assessment Using The Conditional Range Metric written by Thekla Boutsika and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
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%.
Renewable Energy Forecasting
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Author : Georges Kariniotakis
language : en
Publisher: Woodhead Publishing
Release Date : 2017-09-29
Renewable Energy Forecasting written by Georges Kariniotakis and has been published by Woodhead Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-29 with Technology & Engineering categories.
Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects. Offers comprehensive coverage of wind, solar, wave, and hydropower forecasting in one convenient volume Addresses a topic that is growing in importance, given the increasing penetration of renewable energy in many countries Reviews state-of-the-science techniques for renewable energy forecasting Contains chapters on operational applications
Wind Forecasting In Railway Engineering
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Author : Hui Liu
language : en
Publisher: Elsevier
Release Date : 2021-06-17
Wind Forecasting In Railway Engineering written by Hui Liu and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-17 with Technology & Engineering categories.
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
Analysis Of Variability And Uncertainty In Wind Power Forecasting An International Comparison Preprint
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Author :
language : en
Publisher:
Release Date : 2013
Analysis Of Variability And Uncertainty In Wind Power Forecasting An International Comparison Preprint written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
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.