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Modeling and Stochastic Learning for Forecasting in High Dimensions

Author: Anestis Antoniadis

Publisher:

Published: 2015

Total Pages: 339

ISBN-13: 9783319187334

The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive advances in observational capabilities and measurement procedures. On June 5-7, 2013, an international Workshop on Industry Practices for FORecasting was held in Paris, France, organized and supported by the OSIRIS Department of Electricité de France Research and Development Division. In keeping with tradition, both theoretical statistical results and practical contributions on this active field of statistical research and on forecasting issues in a rapidly evolving industrial environment are presented. The volume reflects the broad spectrum of the conference, including 16 articles contributed by specialists in various areas. The material compiled is broad in scope and ranges from new findings on forecasting in industry and in time series, on nonparametric and functional methods, and on on-line machine learning for forecasting, to the latest developments in tools for high dimension and complex data analysis.

Modeling and Stochastic Learning for Forecasting in High Dimensions

Author: Anestis Antoniadis

Publisher: Springer

Published: 2015-06-04

Total Pages: 339

ISBN-13: 3319187325

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The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive advances in observational capabilities and measurement procedures. On June 5-7, 2013, an international Workshop on Industry Practices for Forecasting was held in Paris, France, organized and supported by the OSIRIS Department of Electricité de France Research and Development Division. In keeping with tradition, both theoretical statistical results and practical contributions on this active field of statistical research and on forecasting issues in a rapidly evolving industrial environment are presented. The volume reflects the broad spectrum of the conference, including 16 articles contributed by specialists in various areas. The material compiled is broad in scope and ranges from new findings on forecasting in industry and in time series, on nonparametric and functional methods and on on-line machine learning for forecasting, to the latest developments in tools for high dimension and complex data analysis.

An Introduction to Stochastic Modeling

Author: Howard M. Taylor

Publisher: Academic Press

Published: 2014-05-10

Total Pages: 410

ISBN-13: 1483269272

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An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting

Author: Anuradha Tomar

Publisher: Springer Nature

Published: 2023-01-20

Total Pages: 208

ISBN-13: 9811964904

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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.

Machine Learning and Knowledge Discovery in Databases

Author: Michele Berlingerio

Publisher: Springer

Published: 2019-01-17

Total Pages: 740

ISBN-13: 3030109259

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The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches

Author: Antonio Lepore

Publisher: Springer Nature

Published: 2022-10-19

Total Pages: 130

ISBN-13: 3031124022

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This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.

Renewable Energy: Forecasting and Risk Management

Author: Philippe Drobinski

Publisher: Springer

Published: 2018-12-27

Total Pages: 246

ISBN-13: 3319990527

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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.

Advances in Intelligent Data Analysis XVIII

Author: Michael R. Berthold

Publisher: Springer Nature

Published: 2020-04-22

Total Pages: 601

ISBN-13: 3030445844

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This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.

Solar Irradiance and Photovoltaic Power Forecasting

Author: Dazhi Yang

Publisher: CRC Press

Published: 2024-02-05

Total Pages: 682

ISBN-13: 1003830854

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Forecasting plays an indispensable role in grid integration of solar energy, which is an important pathway toward the grand goal of achieving planetary carbon neutrality. This rather specialized field of solar forecasting constitutes both irradiance and photovoltaic power forecasting. Its dependence on atmospheric sciences and implications for power system operations and planning make the multi-disciplinary nature of solar forecasting immediately obvious. Advances in solar forecasting represent a quiet revolution, as the landscape of solar forecasting research and practice has dramatically advanced as compared to just a decade ago. Solar Irradiance and Photovoltaic Power Forecasting provides the reader with a holistic view of all major aspects of solar forecasting: the philosophy, statistical preliminaries, data and software, base forecasting methods, post-processing techniques, forecast verification tools, irradiance-to-power conversion sequences, and the hierarchical and firm forecasting framework. The book’s scope and subject matter are designed to help anyone entering the field or wishing to stay current in understanding solar forecasting theory and applications. The text provides concrete and honest advice, methodological details and algorithms, and broader perspectives for solar forecasting. Both authors are internationally recognized experts in the field, with notable accomplishments in both academia and industry. Each author has many years of experience serving as editors of top journals in solar energy meteorology. The authors, as forecasters, are concerned not merely with delivering the technical specifics through this book, but more so with the hopes of steering future solar forecasting research in a direction that can truly expand the boundary of forecasting science.

Applications of Artificial Intelligence in Planning and Operation of Smart Grids

Author: Mehdi Rahmani-Andebili

Publisher: Springer Nature

Published: 2022-03-26

Total Pages: 140

ISBN-13: 3030945227

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Artificial intelligence (AI) is going to play a significant role in smart grid planning and operation, especially in solving its real-time problems, as it is fast, adaptive, robust, and less dependent on the system’s accurate model and parameters. This collection covers research advancements in the application of AI in the planning and operation of smart grids. A global group of researchers and scholars present innovative approaches to AI-based smart grid planning and operation, cover the theoretical concepts and experimental results of the application of AI-based techniques, and apply these techniques to deal with smart grid issues. Applications of Artificial Intelligence in Planning and Operation of Smart Grids is an ideal resource for researchers on the theory and application of AI, practicing engineers working in electrical power engineering, and students in advanced graduate-level courses.