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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.
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.
This two-volume set LNCS 6691 and 6692 constitutes the refereed proceedings of the 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, held in Torremolinos-Málaga, Spain, in June 2011. The 154 revised papers were carefully reviewed and selected from 202 submissions for presentation in two volumes. The first volume includes 69 papers organized in topical sections on mathematical and theoretical methods in computational intelligence; learning and adaptation; bio-inspired systems and neuro-engineering; hybrid intelligent systems; applications of computational intelligence; new applications of brain-computer interfaces; optimization algorithms in graphic processing units; computing languages with bio-inspired devices and multi-agent systems; computational intelligence in multimedia processing; and biologically plausible spiking neural processing.
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
"Environmental considerations in addition to energy crises have forced many countries to consider alternative energy sources; renewable energies are known as the best alternatives. Among renewable energies, wind power is the most promising energy source. The chaotic nature of the wind is a major challenge against the integration of wind power into grids. Integration of wind power results in several problems due to the fluctuations inherent in wind power, such as power quality, stability, and dispatch issues. The prediction accuracy of wind power affects its integration into power systems. Several wind power forecasting techniques have been proposed and developed. However, not all of them are able to provide sufficient accuracy. The main contribution of this thesis is to provide accurate short-term wind power prediction. A simple, yet effective adaptiveparameter regression model is developed. Specifically, the proposed approach uses a window of previous observations to obtain the model parameters that minimizes the prediction error. Regression-based models are affected by measurement errors. Thus, other models with the capability of moderating the impact of measurement errors are needed. In order to cope with such errors, two hybrid grey-based short-term wind power prediction techniques are proposed: GM(1,1)-ARMA and GM(1,1)-NARnet. These techniques are combined with ARMA models and Nonlinear Auto Regressive Neural Network (NARnet) models, respectively. GM(1,1)-ARMA and GM(1,1)-NARnet are applied to wind power data and the obtained results are compared with those obtained from ARMA, the traditional grey model, as well as the persistent model. The efficiency of both of the proposed techniques is confirmed. In contrast to the GM(1,1)-ARMA model, the GM(1,1)-NARnet model utilizes the nonlinear components of wind power during the forecasting procedure which results in more accurate prediction."--Abstract.
ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
Beskriver resultatet af et europæisk samarbejde inden for EU vedr. vindenergiens udnyttelse ved at beskrive vindressourcen og de forskellige terraintypers indflydelse herpå.