Download or read book Photovoltaic and Load Forecasting Predictions for a Microgrid Using Machine Learning written by Nelson Mauricio Flores and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Load forecasting has always been an integral part of plant operation. Energy usage is tied to economics and national prosperity. The power grid is in a perpetual state of balance, with the customers consuming the exact amount of energy the utility provides. Knowing the load forecast allows for proper planning and dispatching of energy generation. Over the past several decades, power planners have only been worried about the unknown being load demand. Generation of energy was always assumed to be dispatchable on-demand, like coal, natural gas, etc., due to the abundant supply, which helps also keep energy prices low. Predicting solar irradiation and demand is a challenging task. In this thesis, several methods used in the literature for forecasting load and sun irradiance, such as Auto-Regressive Integrated Moving Average, Moving Average, Long Short-Term Memory, and Support Vector Machines are investigated. Atwo-layer battery management algorithm is used with historical data for load and sun irradiance of a large facility in Downtown Los Angeles to compare these methods in a practical setting. Both load and sun irradiance forecasting methods have been further categorized into short-term and long-term to be used in each layer of the controller. After finding the best algorithm in each category, they have been further optimized to improve their accuracy, and then the two-layer algorithm with optimized forecasts is compared with a conventional peak shaving method.

Download or read book Engineering Optimization written by Xin-She Yang and published by John Wiley & Sons. This book was released on 2010-07-20 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms. The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts: Foundations of Optimization and Algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method Metaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB® and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail. Engineering Optimization: An Introduction with Metaheuristic Applications is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.

Download or read book Solar Irradiance Forecasting Using Hybrid Ensemble Machine Learning Technique written by Josalin Jemima J and published by Mohammed Abdul Sattar. This book was released on 2024-01-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic development is impacted significantly by conventional energy sources, which are hazardous to humans and the environment. To meet the energy demand and reduce greenhouse gas emissions, the world is shifting towards alternate renewable energy sources. Photovoltaics (PV) is the most common distributed energy source for microgrid formation and one of the world's top renewable energy sources because of their modular design, minimal operational noise, and ease of maintenance. Solar photovoltaic systems, which are photovoltaic panels that turn sunlight into electricity, are one of the most common renewable energy sources. PV production is strongly dependent on solar irradiation, temperature, and other weather conditions. Predicting solar irradiance implies predicting solar power generation one or more steps ahead of time. Prediction increases photovoltaic system development and operation while providing numerous economic benefits to energy suppliers. There are numerous applications that employ prediction to improve power grid operation and planning, with the appropriate time-resolution of the forecast. Stability and regulation necessitate knowledge of solar irradiation over the following few seconds. Reserve management and load following require knowledge of solar irradiation for the next several minutes or hours. To function properly, scheduling and unit commitment requires knowledge about the next few days of solar irradiation. It is crucial to precisely measure solar irradiation since the major issue with solar energy is that it fluctuates because of its variability. Grid operators can control the demand and supply of power and construct the best solar PV plant with the help of accurate and reliable solar irradiance predictions. Electric utilities must generate enough energy to balance supply and demand. The electric sector has consequently focused on Solar PV forecasting to assist its management system, which is crucial for the growth of additional power generation, such as microgrids. Forecasting solar irradiance has always been important to renewable energy generation since solar energy generation is location and time-specific. When the estimated solar generation is available, the grid will function more consistently in unpredictable situations since solar energy generates some quantity of power every day of the year, even on cloudy days.

Download or read book Applications of AI and IOT in Renewable Energy written by Rabindra Nath Shaw and published by Academic Press. This book was released on 2022-02-09 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of AI and IOT in Renewable Energy provides a future vision of unexplored areas and applications for Artificial Intelligence and Internet of Things in sustainable energy systems. The ideas presented in this book are backed up by original, unpublished technical research results covering topics like smart solar energy systems, intelligent dc motors and energy efficiency study of electric vehicles. In all these areas and more, applications of artificial intelligence methods, including artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above in hybrid systems are included. This book is designed to assist with developing low cost, smart and efficient solutions for renewable energy systems and is intended for researchers, academics and industrial communities engaged in the study and performance prediction of renewable energy systems. Includes future applications of AI and IOT in renewable energy Based on case studies to give each chapter real-life context Provides advances in renewable energy using AI and IOT with technical detail and data

Download or read book Renewable Energy Forecasting written by Georges Kariniotakis and published by Woodhead Publishing. This book was released on 2017-09-29 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Modeling and Forecasting Electricity Loads and Prices written by Rafal Weron and published by John Wiley & Sons. This book was released on 2007-01-30 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an in-depth and up-to-date review of different statistical tools that can be used to analyze and forecast the dynamics of two crucial for every energy company processes—electricity prices and loads. It provides coverage of seasonal decomposition, mean reversion, heavy-tailed distributions, exponential smoothing, spike preprocessing, autoregressive time series including models with exogenous variables and heteroskedastic (GARCH) components, regime-switching models, interval forecasts, jump-diffusion models, derivatives pricing and the market price of risk. Modeling and Forecasting Electricity Loads and Prices is packaged with a CD containing both the data and detailed examples of implementation of different techniques in Matlab, with additional examples in SAS. A reader can retrace all the intermediate steps of a practical implementation of a model and test his understanding of the method and correctness of the computer code using the same input data. The book will be of particular interest to the quants employed by the utilities, independent power generators and marketers, energy trading desks of the hedge funds and financial institutions, and the executives attending courses designed to help them to brush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and finance wanting to get a grip on advanced statistical tools applied in this hot area. In fact, there are sixteen Case Studies in the book making it a self-contained tutorial to electricity load and price modeling and forecasting.

Download or read book 2020 IEEE International Conference on Power Electronics Drives and Energy Systems PEDES written by IEEE Staff and published by . This book was released on 2020-12-16 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: IEEE Power Electronics, Drives and Energy Systems (PEDES) is a biennial conference sponsored by four Societies of IEEE the Industry Applications Society, Power Electronics Society, Industrial Electronics Society, and Power and Energy Society The first edition of the conference was held in 1996 at New Delhi, India It has subsequently been held in Perth Australia (1998), New Delhi (2006 & 2010), Bengaluru (2012), Mumbai (2014), Thiruvananthapuram (2016), Chennai (2018), and the ninth in the series i e IEEE PEDES 2020 will be held at Malaviya National Institute of Technology (MNIT) Jaipur, Rajasthan from 15 to 18 December 2020 The goal of PEDES is to provide a platform for conference, exhibition and tutorials on selected topics of contemporary interest The conference provides a unique forum for the exchange of knowledge and information among the professionals across the globe in the areas of power electronics, drives and energy systems

Download or read book Modelling and Forecasting of Photovoltaic Generation for Microgrid Applications from Theory to Validation written by Enrica Scolari and published by . This book was released on 2019 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: Microgrids ; Photovoltaics ; Solar forecasting ; Irradiance estimation ; Prediction intervals ; Stochastic resources ; Predictive algorithms ; All-sky imager ; Machine learning ; Artificial intelligence.

Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Federico Divina and published by MDPI. This book was released on 2021-08-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.

Download or read book Ensemble Forecasting Applied to Power Systems written by Antonio Bracale and published by MDPI. This book was released on 2020-03-10 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Short Term Load Forecasting by Artificial Intelligent Technologies written by Wei-Chiang Hong and published by MDPI. This book was released on 2019-01-29 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies

Download or read book Microgrids written by Amjad Anvari-Moghaddam and published by MDPI. This book was released on 2021-05-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems.

Download or read book Microgrid Protection and Control written by Dehua Zheng and published by Academic Press. This book was released on 2021-06-23 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microgrid Protection and Control is the result of numerous research works and publications by R&D engineers and scientists of the Microgrid and Energy Internet Research Centre. Through the authors long-routed experience in the microgrid and energy internet industry, this book looks at the sophisticated protection and control issues connected to the special nature of microgrid. The book explains the different ways of classifying types of microgrids and common misconceptions, looking at industrial and research trends along with the different technical issues and challenges faced with deploying microgrid in various settings. Forecasting short-term demand and renewable generation for optimal operation is covered with techniques for accurate enhancement supported with practical application examples. With chapters on dynamic, transient and tertiary control and experimental and simulation tests this reference is useful for all those working in the research, engineering and application of microgrids and power distribution systems. Contains practical examples to support the research and experimental results on microgrid protection and control Includes detailed theories and referential algorithms Provides innovative solutions to technical issues in protection and control of microgrids

Download or read book Photovoltaic Power Analysis and Prediction Using Machine Learning Methods written by Halah Shehada and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The stochastic nature of Photovoltaic power directly affects the stability of the grid. PV power forecasting allows power stations to know beforehand how much PV power will be available, which ensures that the grid remains in stabilized condition. PV power from India is analyzed and predicted using machine learning methods

Download or read book Machine Learning and the Internet of Things in Solar Power Generation written by Prabha Umapathy and published by CRC Press. This book was released on 2023-07-14 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book investigates various MPPT algorithms, and the optimization of solar energy using machine learning and deep learning. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental. This book: Discusses data acquisition by the internet of things for real-time monitoring of solar cells. Covers artificial neural network techniques, solar collector optimization, and artificial neural network applications in solar heaters, and solar stills. Details solar analytics, smart centralized control centers, integration of microgrids, and data mining on solar data. Highlights the concept of asset performance improvement, effective forecasting for energy production, and Low-power wide-area network applications. Elaborates solar cell design principles, the equivalent circuits of single and two diode models, measuring idealist factors, and importance of series and shunt resistances. The text elaborates solar cell design principles, the equivalent circuit of single diode model, the equivalent circuit of two diode model, measuring idealist factor, and importance of series and shunt resistances. It further discusses perturb and observe technique, modified P&O method, incremental conductance method, sliding control method, genetic algorithms, and neuro-fuzzy methodologies. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental.

Download or read book Forecasting and Assessing Risk of Individual Electricity Peaks written by Maria Jacob and published by Springer Nature. This book was released on 2019-09-25 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Weather Modeling and Forecasting of PV Systems Operation written by Marius Paulescu and published by Springer Science & Business Media. This book was released on 2012-11-05 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, there has been a substantial increase of grid-feeding photovoltaic applications, thus raising the importance of solar electricity in the energy mix. This trend is expected to continue and may even increase. Apart from the high initial investment cost, the fluctuating nature of the solar resource raises particular insertion problems in electrical networks. Proper grid managing demands short- and long-time forecasting of solar power plant output. Weather modeling and forecasting of PV systems operation is focused on this issue. Models for predicting the state of the sky, nowcasting solar irradiance and forecasting solar irradiation are studied and exemplified. Statistical as well as artificial intelligence methods are described. The efficiency of photovoltaic converters is assessed for any weather conditions. Weather modeling and forecasting of PV systems operation is written for researchers, engineers, physicists and students interested in PV systems design and utilization. “p>