Artificial intelligence enabled computational methods for smart grid forecast and dispatch, Yuanzheng Li, Yong Zhao,
Новое издание
Автор: Yuanzheng Li, Yong Zhao Название: Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch ISBN: 9819907985 ISBN-13(EAN): 9789819907984 Издательство: Springer Цена: 22359.00 р. Наличие на складе: Есть у поставщикаПоставка под заказ. Описание: With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts. (1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch. (2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast. (3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch. The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.
Описание: With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts. (1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch. (2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast. (3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch. The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.
In The Battle for Veterans' Healthcare, award-winning author Suzanne Gordon takes us to the front lines of federal policymaking and healthcare delivery, as it affects eight million Americans whose military service makes them eligible for Veterans Health Administration (VHA) coverage.
Gordon’s collected dispatches provide insight and information too often missing from mainstream media reporting on the VHA and from Capitol Hill debates about its future. Drawing on interviews with veterans and their families, VHA staff and administrators, health care policy experts and Congressional decision makers, Gordon describes a federal agency under siege that nevertheless accomplishes its difficult mission of serving men and women injured, in myriad ways, while on active duty.
The Battle for Veterans’ Healthcare is an essential primer on VHA care and a call to action by veterans, their advocacy organizations, and political allies. Without lobbying efforts and broader public understanding of what’s at stake, a system now functioning far better than most private hospital systems may end up looking more like them, to the detriment of patients and providers alike.
Автор: Kaiser, Mark Название: Decommissioning Forecasting and Operating Cost Estimation ISBN: 0128181133 ISBN-13(EAN): 9780128181133 Издательство: Elsevier Science Рейтинг: Цена: 19875.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The US Gulf of Mexico is one of the largest and most prolific offshore hydrocarbon basins in the world with thousands of structures installed in the region and tens of thousands of wells drilled. Over the past decade, a significant number of structures in shallow water have been decommissioned, as operators can no longer "kick the decommissioning can" down the road. This has opened up new markets and additional regulatory oversight with far-reaching implications. This book describes future decommissioning trends and issues and provides guidance for operator budgeting, regulatory oversight, and service sector companies interested in participating in the field.
Decommissioning Forecasting and Operating Cost Estimation is the first of its kind textbook to develop models to forecast platform decommissioning in the Gulf of Mexico and to better understand the dynamics of offshore production cost. The book bridges the gap between modeling and technical knowledge to provide insight into the sector. Topics are presented in five parts covering fundamentals, structure inventories and well trends, decommissioning modeling, critical infrastructure issues, and operating cost estimation. Factor models and activity-based cost models in operating cost estimation conclude the discussion.
Decommissioning Forecasting and Operating Cost Estimation helps oil and gas professionals navigate through this complex and challenging field providing an invaluable resource for academics, researchers, and professionals. The book will also serve government regulators, energy and environmental engineers, offshore managers, financial analyst, and others interested in this fascinating and dynamic industry.
In-depth economic, statistical, and systems analysis on Gulf of Mexico decommissioning activity
Balanced coverage of fundamental knowledge and advanced methods
Delivers data and results to understand infrastructure and activity trends
Numerous examples, worked-out problems, and real-world applications
Engineering, science, and market perspectives
Автор: Bahman Zohuri, Farhang Mossavar Rahmani and Farahn Название: Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm ISBN: 0323951120 ISBN-13(EAN): 9780323951128 Издательство: Elsevier Science Рейтинг: Цена: 26107.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Supervision in Neuropsychology offers a review of theoretical, practical, and ethical considerations for professionals providing supervision in clinical neuropsychology. The book covers competency, structural and practical issues, ethical considerations, diversity and inclusion in supervision, future challenges, and more. It concludes with 8 appendices for easy reference.
Автор: Ajoy K. Palit; Dobrivoje Popovic Название: Computational Intelligence in Time Series Forecasting ISBN: 1849969701 ISBN-13(EAN): 9781849969703 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Foresight in an engineering business can make the difference between success and failure, and can be vital to the effective control of industrial systems. The authors of this book harness the power of intelligent technologies individually and in combination.
Описание: This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling.
Автор: Pritpal Singh Название: Applications of Soft Computing in Time Series Forecasting ISBN: 3319262920 ISBN-13(EAN): 9783319262925 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach.
Описание: This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling.
Become proficient in deriving insights from time-series data and analyzing a model's performance
Key Features:
Explore popular and modern machine learning methods including the latest online and deep learning algorithms
Learn to increase the accuracy of your predictions by matching the right model with the right problem
Master time-series via real-world case studies on operations management, digital marketing, finance, and healthcare
Book Description:
Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.
This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.
Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.
By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.
What You Will Learn:
Understand the main classes of time-series and learn how to detect outliers and patterns
Choose the right method to solve time-series problems
Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
Get to grips with time-series data visualization
Understand classical time-series models like ARMA and ARIMA
Implement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning models
Become familiar with many libraries like prophet, xgboost, and TensorFlow
Who this book is for:
This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.
