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Deep Learning For Electricity Forecasting Using Time Series Data
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Book Synopsis Deep Learning for Time Series Forecasting by : Jason Brownlee
Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
Book Synopsis The Laplace Distribution and Generalizations by : Samuel Kotz
Download or read book The Laplace Distribution and Generalizations written by Samuel Kotz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the inferential and modeling advantages that this distribution, together with its generalizations and modifications, offers. The exposition systematically unfolds with many examples, tables, illustrations, and exercises. A comprehensive index and extensive bibliography also make this book an ideal text for a senior undergraduate and graduate seminar on statistical distributions, or for a short half-term academic course in statistics, applied probability, and finance.
Book Synopsis Renewable Energy Forecasting by : Georges Kariniotakis
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
Book Synopsis Biomedical Applications Based on Natural and Artificial Computing by : José Manuel Ferrández Vicente
Download or read book Biomedical Applications Based on Natural and Artificial Computing written by José Manuel Ferrández Vicente and published by Springer. This book was released on 2017-06-10 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volumes LNCS 10337 and 10338 constitute the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, held in Corunna, Spain, in June 2017. The total of 102 full papers was carefully reviewed and selected from 194 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on natural and artificial computation for biomedicine and neuroscience, addressing topics such as theoretical neural computation; models; natural computing in bioinformatics; physiological computing in affective smart environments; emotions; as well as signal processing and machine learning applied to biomedical and neuroscience applications. The second volume deals with biomedical applications, based on natural and artificial computing and addresses topics such as biomedical applications; mobile brain computer interaction; human robot interaction; deep learning; machine learning applied to big data analysis; computational intelligence in data coding and transmission; and applications.
Book Synopsis International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 by : Manuel Graña
Download or read book International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 written by Manuel Graña and published by Springer. This book was released on 2018-06-06 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes papers presented at SOCO 2018, CISIS 2018 and ICEUTE 2018, all held in the beautiful and historic city of San Sebastian (Spain), in June 2018. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze highly complex issues and phenomena. After a rigorous peer-review process, the 13th SOCO 2018 International Program Committee selected 41 papers, with a special emphasis on optimization, modeling and control using soft computing techniques and soft computing applications in the field of industrial and environmental enterprises. The aim of the 11th CISIS 2018 conference was to offer a meeting opportunity for academic and industry researchers from the vast areas of computational intelligence, information security, and data mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, was the catalyst for the overall event.Eight of the papers included in the book were selected by the CISIS 2018 International Program Committee. The International Program Committee of ICEUTE 2018 selected 11 papers for inclusion in these conference proceedings.
Book Synopsis Forecasting and Assessing Risk of Individual Electricity Peaks by : Maria Jacob
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.
Book Synopsis Electrical Load Forecasting by : S.A. Soliman
Download or read book Electrical Load Forecasting written by S.A. Soliman and published by Elsevier. This book was released on 2010-05-26 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Machine Learning and Information Processing by : Debabala Swain
Download or read book Machine Learning and Information Processing written by Debabala Swain and published by Springer Nature. This book was released on 2021-04-02 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes selected papers from the 2nd International Conference on Machine Learning and Information Processing (ICMLIP 2020), held at Vardhaman College of Engineering, Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, from November 28 to 29, 2020. It presents the latest developments and technical solutions in the areas of advanced computing and data sciences, covering machine learning, artificial intelligence, human–computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.
Book Synopsis Artificial Intelligence and Soft Computing by : Leszek Rutkowski
Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer. This book was released on 2018-05-11 with total page 774 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 10841 and LNAI 10842 constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, held in Zakopane, Poland in June 2018. The 140 revised full papers presented were carefully reviewed and selected from 242 submissions. The papers included in the first volume are organized in the following three parts: neural networks and their applications; evolutionary algorithms and their applications; and pattern classification.
Book Synopsis Statistics for Machine Learning by : Pratap Dangeti
Download or read book Statistics for Machine Learning written by Pratap Dangeti and published by Packt Publishing Ltd. This book was released on 2017-07-21 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Book Synopsis Artificial Intelligence for Renewable Energy Systems by : Ajay Kumar Vyas
Download or read book Artificial Intelligence for Renewable Energy Systems written by Ajay Kumar Vyas and published by John Wiley & Sons. This book was released on 2022-03-02 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Deep Learning with Python by : Francois Chollet
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Book Synopsis Smart Meter Data Analytics by : Yi Wang
Download or read book Smart Meter Data Analytics written by Yi Wang and published by Springer Nature. This book was released on 2020-02-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.
Book Synopsis Advances in Computing and Data Sciences by : Mayank Singh
Download or read book Advances in Computing and Data Sciences written by Mayank Singh and published by Springer. This book was released on 2018-10-25 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (CCIS 905 and CCIS 906) constitutes the refereed proceedings of the Second International Conference on Advances in Computing and Data Sciences, ICACDS 2018, held in Dehradun, India, in April 2018. The 110 full papers were carefully reviewed and selected from 598 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations.
Book Synopsis Applications of Computing and Communication Technologies by : Ganesh Chandra Deka
Download or read book Applications of Computing and Communication Technologies written by Ganesh Chandra Deka and published by Springer. This book was released on 2018-08-29 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book (CCIS 899) constitutes the refereed proceedings of the First International Conference on Applications of Computing and Communication Technologies, ICACCT 2018, held in Delhi, India, in March 2018. The 30 full papers were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on communication and system technologies, computing and network technologies, application and services.
Book Synopsis Amazon Web Services in Action by : Michael Wittig
Download or read book Amazon Web Services in Action written by Michael Wittig and published by Simon and Schuster. This book was released on 2018-09-15 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Amazon Web Services in Action, Second Edition is a comprehensive introduction to computing, storing, and networking in the AWS cloud. You'll find clear, relevant coverage of all the essential AWS services you to know, emphasizing best practices for security, high availability and scalability. Foreword by Ben Whaley, AWS community hero and author. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The largest and most mature of the cloud platforms, AWS offers over 100 prebuilt services, practically limitless compute resources, bottomless secure storage, as well as top-notch automation capabilities. This book shows you how to develop, host, and manage applications on AWS. About the Book Amazon Web Services in Action, Second Edition is a comprehensive introduction to deploying web applications in the AWS cloud. You'll find clear, relevant coverage of all essential AWS services, with a focus on automation, security, high availability, and scalability. This thoroughly revised edition covers the latest additions to AWS, including serverless infrastructure with AWS Lambda, sharing data with EFS, and in-memory storage with ElastiCache. What's inside Completely revised bestseller Secure and scale distributed applications Deploy applications on AWS Design for failure to achieve high availability Automate your infrastructure About the Reader Written for mid-level developers and DevOps engineers. About the Author Andreas Wittig and Michael Wittig are software engineers and DevOps consultants focused on AWS. Together, they migrated the first bank in Germany to AWS in 2013. Table of Contents PART 1 - GETTING STARTED What is Amazon Web Services? A simple example: WordPress in five minutes PART 2 - BUILDING VIRTUAL INFRASTRUCTURE CONSISTING OF COMPUTERS AND NETWORKING Using virtual machines: EC2 Programming your infrastructure: The command-line, SDKs, and CloudFormation Automating deployment: CloudFormation, Elastic Beanstalk, and OpsWorks Securing your system: IAM, security groups, and VPC Automating operational tasks with Lambda PART 3 - STORING DATA IN THE CLOUD Storing your objects: S3 and Glacier Storing data on hard drives: EBS and instance store Sharing data volumes between machines: EFS Using a relational database service: RDS Caching data in memory: Amazon ElastiCache Programming for the NoSQL database service: DynamoDB PART 4 - ARCHITECTING ON AWS Achieving high availability: availability zones, auto-scaling, and CloudWatch Decoupling your infrastructure: Elastic Load Balancing and Simple Queue Service Designing for fault tolerance Scaling up and down: auto-scaling and CloudWatch
Book Synopsis Innovative Mobile and Internet Services in Ubiquitous Computing by : Leonard Barolli
Download or read book Innovative Mobile and Internet Services in Ubiquitous Computing written by Leonard Barolli and published by Springer. This book was released on 2019-06-18 with total page 950 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the latest research findings, methods and techniques, as well as challenges and solutions related to Ubiquitous and Pervasive Computing (UPC). In this regard, it employs both theoretical and practical perspectives, and places special emphasis on innovative, mobile and internet services. With the proliferation of wireless technologies and electronic devices, there is a rapidly growing interest in Ubiquitous and Pervasive Computing (UPC). UPC makes it possible to create a human-oriented computing environment in which computer chips are embedded in everyday objects and interact with the physical world. Through UPC, people can remain online even while underway, thus enjoying nearly permanent access to their preferred services. Though it has a great potential to revolutionize our lives, UPC also poses a number of new research challenges.
