Prediction Techniques For Renewable Energy Generation And Load Demand Forecasting

Author : Anuradha Tomar,Prerna Gaur,Xiaolong Jin
Publisher : Springer Nature
Page : 208 pages
File Size : 55,5 Mb
Release : 2023-01-20
Category : Technology & Engineering
ISBN : 9789811964909

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Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting by Anuradha Tomar,Prerna Gaur,Xiaolong Jin Pdf

This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.

Author : Georges Kariniotakis
Publisher : Woodhead Publishing
Page : 386 pages
File Size : 54,6 Mb
Release : 2017-09-29
Category : Technology & Engineering
ISBN : 9780081005057

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Renewable Energy Forecasting by Georges Kariniotakis Pdf

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

Author : William Holderbaum,Feras Alasali,Ayush Sinha
Publisher : Springer Nature
Page : 218 pages
File Size : 41,8 Mb
Release : 2023-01-07
Category : Technology & Engineering
ISBN : 9783030828486

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Energy Forecasting and Control Methods for Energy Storage Systems in Distribution Networks by William Holderbaum,Feras Alasali,Ayush Sinha Pdf

This book describes the stochastic and predictive control modelling of electrical systems that can meet the challenge of forecasting energy requirements under volatile conditions. The global electrical grid is expected to face significant energy and environmental challenges such as greenhouse emissions and rising energy consumption due to the electrification of heating and transport. Today, the distribution network includes energy sources with volatile demand behaviour, and intermittent renewable generation. This has made it increasingly important to understand low voltage demand behaviour and requirements for optimal energy management systems to increase energy savings, reduce peak loads, and reduce gas emissions. Electrical load forecasting is a key tool for understanding and anticipating the highly stochastic behaviour of electricity demand, and for developing optimal energy management systems. Load forecasts, especially of the probabilistic variety, can support more informed planning and management decisions, which will be essential for future low carbon distribution networks. For storage devices, forecasts can optimise the appropriate state of control for the battery. There are limited books on load forecasts for low voltage distribution networks and even fewer demonstrations of how such forecasts can be integrated into the control of storage. This book presents material in load forecasting, control algorithms, and energy saving and provides practical guidance for practitioners using two real life examples: residential networks and cranes at a port terminal.

Author : Stephen Haben,Marcus Voss,William Holderbaum
Publisher : Springer Nature
Page : 332 pages
File Size : 41,6 Mb
Release : 2023-06-01
Category : Technology & Engineering
ISBN : 9783031278525

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Core Concepts and Methods in Load Forecasting by Stephen Haben,Marcus Voss,William Holderbaum Pdf

This comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code). Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital. This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.

Author : João P. S. Catalão
Publisher : CRC Press
Page : 462 pages
File Size : 54,6 Mb
Release : 2017-12-19
Category : Science
ISBN : 9781439893968

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Electric Power Systems by João P. S. Catalão Pdf

Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling helps readers develop their skills in modeling, simulating, and optimizing electric power systems. Carefully balancing theory and practice, it presents novel, cutting-edge developments in forecasting and scheduling. The focus is on understanding and solving pivotal problems in the management of electric power generation systems. Methods for Coping with Uncertainty and Risk in Electric Power Generation Outlining real-world problems, the book begins with an overview of electric power generation systems. Since the ability to cope with uncertainty and risk is crucial for power generating companies, the second part of the book examines the latest methods and models for self-scheduling, load forecasting, short-term electricity price forecasting, and wind power forecasting. Toward Optimal Coordination between Hydro, Thermal, and Wind Power Using case studies, the third part of the book investigates how to achieve the most favorable use of available energy sources. Chapters in this section discuss price-based scheduling for generating companies, optimal scheduling of a hydro producer, hydro-thermal coordination, unit commitment with wind generators, and optimal optimization of multigeneration systems. Written in a pedagogical style that will appeal to graduate students, the book also expands on research results that are useful for engineers and researchers. It presents the latest techniques in increasingly important areas of power system operations and planning.

Author : Philippe Drobinski,Mathilde Mougeot,Dominique Picard,Riwal Plougonven,Peter Tankov
Publisher : Springer
Page : 246 pages
File Size : 44,8 Mb
Release : 2018-12-27
Category : Mathematics
ISBN : 9783319990521

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Renewable Energy: Forecasting and Risk Management by Philippe Drobinski,Mathilde Mougeot,Dominique Picard,Riwal Plougonven,Peter Tankov Pdf

Gathering selected, revised and extended contributions from the conference ‘Forecasting and Risk Management for Renewable Energy FOREWER’, which took place in Paris in June 2017, this book focuses on the applications of statistics to the risk management and forecasting problems arising in the renewable energy industry. The different contributions explore all aspects of the energy production chain: forecasting and probabilistic modelling of renewable resources, including probabilistic forecasting approaches; modelling and forecasting of wind and solar power production; prediction of electricity demand; optimal operation of microgrids involving renewable production; and finally the effect of renewable production on electricity market prices. Written by experts in statistics, probability, risk management, economics and electrical engineering, this multidisciplinary volume will serve as a reference on renewable energy risk management and at the same time as a source of inspiration for statisticians and probabilists aiming to work on energy-related problems.

Author : Fouzi Harrou,Ying Sun
Publisher : BoD – Books on Demand
Page : 212 pages
File Size : 50,8 Mb
Release : 2020-04-01
Category : Technology & Engineering
ISBN : 9781838800918

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Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems by Fouzi Harrou,Ying Sun Pdf

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.

Author : S.A. Soliman,Ahmad Mohammad Al-Kandari
Publisher : Elsevier
Page : 440 pages
File Size : 43,8 Mb
Release : 2010-05-26
Category : Business & Economics
ISBN : 0123815444

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Electrical Load Forecasting by S.A. Soliman,Ahmad Mohammad Al-Kandari Pdf

Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world’s foremost experts on the subject, Electrical Load Forecasting provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. The book begins with a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. The book is also supported by an online computer program that allows readers to construct, validate, and run short and long term models. Step-by-step guide to model construction Construct, verify, and run short and long term models Accurately evaluate load shape and pricing Creat regional specific electrical load models

Author : Valentin A. Boicea
Publisher : CRC Press
Page : 62 pages
File Size : 41,8 Mb
Release : 2021-06-27
Category : Computers
ISBN : 9781000437812

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Energy Management by Valentin A. Boicea Pdf

This book introduces the principle of carrying out a medium-term load forecast (MTLF) at power system level, based on the Big Data concept and Convolutionary Neural Network (CNNs). It also presents further research directions in the field of Deep Learning techniques and Big Data, as well as how these two concepts are used in power engineering. Efficient processing and accuracy of Big Data in the load forecast in power engineering leads to a significant improvement in the consumption pattern of the client and, implicitly, a better consumer awareness. At the same time, new energy services and new lines of business can be developed. The book will be of interest to electrical engineers, power engineers, and energy services professionals.

Author : Antonio Bracale,Pasquale De Falco
Publisher : MDPI
Page : 134 pages
File Size : 46,5 Mb
Release : 2020-03-10
Category : Technology & Engineering
ISBN : 9783039283125

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Ensemble Forecasting Applied to Power Systems by Antonio Bracale,Pasquale De Falco Pdf

Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.

Author : Maria Jacob,Cláudia Neves,Danica Vukadinović Greetham
Publisher : Springer Nature
Page : 108 pages
File Size : 50,8 Mb
Release : 2019-09-25
Category : Mathematics
ISBN : 9783030286699

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Forecasting and Assessing Risk of Individual Electricity Peaks by Maria Jacob,Cláudia Neves,Danica Vukadinović Greetham Pdf

The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general.

Author : Gayadhar Panda
Publisher : Springer Nature
Page : 327 pages
File Size : 43,5 Mb
Release : 2024-07-03
Category : Electronic
ISBN : 9789819988860

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Digital Communication and Soft Computing Approaches Towards Sustainable Energy Developments by Gayadhar Panda Pdf

Author : Sarat Kumar Sahoo,Franco Fernando Yanine,Vikram Kulkarni,Akhtar Kalam
Publisher : Frontiers Media SA
Page : 196 pages
File Size : 42,6 Mb
Release : 2023-12-08
Category : Technology & Engineering
ISBN : 9782832541678

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Recent Advances in Renewable Energy Automation and Energy Forecasting by Sarat Kumar Sahoo,Franco Fernando Yanine,Vikram Kulkarni,Akhtar Kalam Pdf

The advancement of sustainable energy is becoming an important concern for many countries. The traditional electrical grid supports only one-way interaction of power being delivered to the consumers. The emergence of improved sensors, actuators, and automation technologies has consequently improved the control, monitoring and communication techniques within the energy sector, including the Smart Grid system. With the support of the aforementioned modern technologies, the information flows in two-ways between the consumer and supplier. This data communication helps the supplier in overcoming challenges like integration of renewable technologies, management of energy demand, load automation and control. Renewable energy (RE) is intermittent in nature and therefore difficult to predict. The accurate RE forecasting is very essential to improve the power system operations. The forecasting models are based on complex function combinations that include seasonality, fluctuation, and dynamic nonlinearity. The advanced intelligent computing algorithms for forecasting should consider the proper parameter determinations for achieving optimization. For this we need, new generation research areas like Machine learning (ML), and Artificial Intelligence (AI) to enable the efficient integration of distributed and renewable generation at large scale and at all voltage levels. The modern research in the above areas will improve the efficiency, reliability and sustainability in the Smart grid.

Author : Josep M. Guerrero,Pankaj Gupta,Ritu Kandari,Alexander Micallef
Publisher : Elsevier
Page : 315 pages
File Size : 53,8 Mb
Release : 2024-02-21
Category : Computers
ISBN : 9780443158070

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Intelligent Learning Approaches for Renewable and Sustainable Energy by Josep M. Guerrero,Pankaj Gupta,Ritu Kandari,Alexander Micallef Pdf

Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy.The book begins by introducing the intelligent learning approaches, and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. The second section of the book provides detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, and optimization, supported by case studies, figures, schematics, and references.This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence. Explores cutting-edge intelligent techniques and their implications for future energy systems development Opens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and more Includes a range of case studies that provide insights into the challenges and solutions in real-world applications

Author : Roya Ahmadiahangar,Argo Rosin,Ivo Palu,Aydin Azizi
Publisher : Springer Nature
Page : 66 pages
File Size : 40,5 Mb
Release : 2020-05-08
Category : Technology & Engineering
ISBN : 9789811546273

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Demand-side Flexibility in Smart Grid by Roya Ahmadiahangar,Argo Rosin,Ivo Palu,Aydin Azizi Pdf

This book highlights recent advances in the identification, prediction and exploitation of demand side (DS) flexibility and investigates new methods of predicting DS flexibility at various different power system (PS) levels. Renewable energy sources (RES) are characterized by volatile, partially unpredictable and mostly non-dispatchable generation. The main challenge in terms of integrating RES into power systems is their intermittency, which negatively affects the power balance. Addressing this challenge requires an increase in the available PS flexibility, which in turn requires accurate estimation of the available flexibility on the DS and aggregation solutions at the system level. This book discusses these issues and presents solutions for effectively tackling them.