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Author: Maria Jacob Publisher: Springer Nature ISBN: 303028669X Category : Mathematics Languages : en Pages : 108
Book Description
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: Maria Jacob Publisher: Springer Nature ISBN: 303028669X Category : Mathematics Languages : en Pages : 108
Book Description
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: Danica Vukadinovic Greetham Publisher: ISBN: 9781013273797 Category : Technology & Engineering Languages : en Pages : 106
Book Description
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. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Author: Stephen Haben Publisher: Springer Nature ISBN: 3031278526 Category : Technology & Engineering Languages : en Pages : 332
Book Description
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: Giuseppe Nicosia Publisher: Springer Nature ISBN: 3030645835 Category : Computers Languages : en Pages : 740
Book Description
This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
Author: Ahteshamul Haque Publisher: CRC Press ISBN: 1000857506 Category : Technology & Engineering Languages : en Pages : 243
Book Description
The current model for electricity generation and distribution is dominated by centralized power plants which are typically associated with combustion (coal, oil, and natural gas) or nuclear generation units. These power models require distribution from the center to outlying consumers and have many disadvantages concerning the electric utilities, transmission and distribution, and greenhouse gas emissions. This resulted in the modelling and development of cleaner renewable power generation with alternative sources such as photovoltaic (PV), wind, and other sources. Further, due to matured PV technology, constant drop-in installation cost, greenhouse emissions reductions, energy efficiency, reduced transmission and distribution investments, minimization of electric losses, and network support, the development of PV systems is proliferating. In view of this development, this book provides an idea for setting up the PV plant from initial study of the site to plan sizing. Once the first planning is covered, the book focuses on the modeling aspects of power electronics converter and control elements associated with it keeping the operating standards specified for the development of distributed generation systems in check. This book will be useful for industrial professionals and researchers who are working toward modeling of PV plants, and their control in grid connected operation. All the necessary information related to these fields is available in the book.
Author: Aboul-Ella Hassanien Publisher: Springer Nature ISBN: 3030442896 Category : Technology & Engineering Languages : en Pages : 880
Book Description
This book presents the proceedings of the 1st International Conference on Artificial Intelligence and Computer Visions (AICV 2020), which took place in Cairo, Egypt, from April 8 to 10, 2020. This international conference, which highlighted essential research and developments in the fields of artificial intelligence and computer visions, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into sections, covering the following topics: swarm-based optimization mining and data analysis, deep learning and applications, machine learning and applications, image processing and computer vision, intelligent systems and applications, and intelligent networks.
Author: Maria Jacob
Author: Maria Jacob
Author: Danica Vukadinovic Greetham
Author: Stephen Haben
Author: Giuseppe Nicosia
Author: Jimson Mathew
Author: Ahteshamul Haque
Author: Aboul-Ella Hassanien
Author: Adela Maria Bolet
Author: Adela Maria Bolet
Author: