Download or read book Feature Papers of Forecasting written by Sonia Leva and published by MDPI. This book was released on 2021-08-06 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented.
Download or read book Forecasting principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Download or read book Feature Papers written by Michael Henson and published by MDPI. This book was released on 2018-10-04 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Feature Papers" that was published in Processes
Download or read book Ecological Forecasting written by Michael C. Dietze and published by Princeton University Press. This book was released on 2017-05-30 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative and accessible introduction to the concepts and tools needed to make ecology a more predictive science Ecologists are being asked to respond to unprecedented environmental challenges. How can they provide the best available scientific information about what will happen in the future? Ecological Forecasting is the first book to bring together the concepts and tools needed to make ecology a more predictive science. Ecological Forecasting presents a new way of doing ecology. A closer connection between data and models can help us to project our current understanding of ecological processes into new places and times. This accessible and comprehensive book covers a wealth of topics, including Bayesian calibration and the complexities of real-world data; uncertainty quantification, partitioning, propagation, and analysis; feedbacks from models to measurements; state-space models and data fusion; iterative forecasting and the forecast cycle; and decision support. Features case studies that highlight the advances and opportunities in forecasting across a range of ecological subdisciplines, such as epidemiology, fisheries, endangered species, biodiversity, and the carbon cycle Presents a probabilistic approach to prediction and iteratively updating forecasts based on new data Describes statistical and informatics tools for bringing models and data together, with emphasis on: Quantifying and partitioning uncertainties Dealing with the complexities of real-world data Feedbacks to identifying data needs, improving models, and decision support Numerous hands-on activities in R available online
Download or read book Making Climate Forecasts Matter written by National Research Council and published by National Academies Press. This book was released on 1999-05-27 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: El Nino has been with us for centuries, but now we can forcast it, and thus can prepare far in advance for the extreme climatic events it brings. The emerging ability to forecast climate may be of tremendous value to humanity if we learn how to use the information well. How does society cope with seasonal-to-interannual climatic variations? How have climate forecasts been usedâ€"and how useful have they been? What kinds of forecast information are needed? Who is likely to benefit from forecasting skill? What are the benefits of better forecasting? This book reviews what we know about these and other questions and identifies research directions toward more useful seasonal-to-interannual climate forecasts. In approaching their recommendations, the panel explores: Vulnerability of human activities to climate. State of the science of climate forecasting. How societies coevolved with their climates and cope with variations in climate. How climate information should be disseminated to achieve the best response. How we can use forecasting to better manage the human consequences of climate change.
Download or read book Machine Learning for Time Series Forecasting with Python written by Francesca Lazzeri and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
Download or read book Advanced Models of Energy Forecasting written by Xun Zhang and published by Frontiers Media SA. This book was released on 2022-11-23 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Neural Networks written by Petia Koprinkova-Hristova and published by Springer. This book was released on 2014-09-02 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.
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.
Download or read book Monthly Energy Review written by and published by . This book was released on 1992-04 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Low Carbon Oriented Improvement Strategy for Flexibility and Resiliency of Multi Energy Systems written by Yumin Zhang and published by Frontiers Media SA. This book was released on 2024-09-18 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the inherent volatility and randomness, the increasing share of energy from renewable resources presents a challenge to the operation of multi-energy systems with heterogeneous energy carriers such as electricity, heat, hydrogen, etc. These factors will make the systems hard to adjust their supply and demand flexibly to maintain energy balance to ensure reliability. Further, this hinders the development of a low-carbon and economically viable energy system. By making full use of the synergistic interaction of generation, transmission, load demand, and energy storage, a three-fold approach focused on quantifying demand flexibility, evaluating supply capabilities, and enhancing resilience can unlock the flexibility potential across various sectors of new energy systems. This approach provides an effective means of facilitating the transition from conventional energy systems to low-carbon, clean-energy-oriented paradigms. However, huge challenges arising from renewable energy pose great obstacles to the aforementioned solution pathway. The main objectives of this Research Topic are： 1. Develop advanced carbon emission accounting and measurement techniques for emerging multi-energy systems 2. Design effective methods for predicting renewable electricity generation 3. Proposed efficient methods for quantitative assessment of uncertainty from renewables and loads 4. Put forward advanced evaluation, optimization, and planning strategies incorporating diverse flexibility resources 5. Design multifaceted market mechanisms and collaborative frameworks balancing economics and low carbon footprint 6. Develop operational control and resilience-enhancement techniques for distribution networks under large-scale distributed energy integration
Download or read book Statistical Reference Index written by and published by . This book was released on 1980 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book ENSO Nonlinearity and Complexity Features Mechanisms Impacts and Prediction written by Hong-Li Ren and published by Frontiers Media SA. This book was released on 2022-08-05 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Algorithms in Advanced Artificial Intelligence written by R. N. V. Jagan Mohan and published by CRC Press. This book was released on 2024-07-08 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most common form of severe dementia, Alzheimer’s disease (AD), is a cumulative neurological disorder because of the degradation and death of nerve cells in the brain tissue, intelligence steadily declines and most of its activities are compromised in AD. Before diving into the level of AD diagnosis, it is essential to highlight the fundamental differences between conventional machine learning (ML) and deep learning (DL). This work covers a number of photo-preprocessing approaches that aid in learning because image processing is essential for the diagnosis of AD. The most crucial kind of neural network for computer vision used in medical image processing is called a Convolutional Neural Network (CNN). The proposed study will consider facial characteristics, including expressions and eye movements using the diffusion model, as part of CNN’s meticulous approach to Alzheimer’s diagnosis. Convolutional neural networks were used in an effort to sense Alzheimer’s disease in its early stages using a big collection of pictures of facial expressions.
Download or read book Advanced Anomaly Detection Technologies and Applications in Energy Systems written by Tinghui Ouyang and published by Frontiers Media SA. This book was released on 2022-10-14 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Exploration of Novel Intelligent Optimization Algorithms written by Kangshun Li and published by Springer Nature. This book was released on 2022-07-31 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Symposium, ISICA 2021, held in Guangzhou, China, during November 19–21, 2021. The 48 full papers included in this book were carefully reviewed and selected from 99 submissions. They were organized in topical sections as follows: new frontier of multi-objective evolutionary algorithms; intelligent multi-media; data modeling and application of artificial intelligence; exploration of novel intelligent optimization algorithm; and intelligent application of industrial production.
Download or read book Advances in Internet Data and Web Technologies written by Leonard Barolli and published by Springer. This book was released on 2019-02-05 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents original contributions on the theories and practices of emerging Internet, Data and Web technologies and their applications in businesses, engineering and academia. As a key feature, it addresses advances in the life-cycle exploitation of data generated by digital ecosystem technologies. The Internet has become the most proliferative platform for emerging large-scale computing paradigms. Among these, Data and Web technologies are two of the most prominent paradigms, manifesting in a variety of forms such as Data Centers, Cloud Computing, Mobile Cloud, Mobile Web Services, and so on. These technologies altogether create a digital ecosystem whose cornerstone is the data cycle, from capturing to processing, analysis and visualization. The need to investigate various research and development issues in this digital ecosystem has been made even more pressing by the ever-increasing demands of real-life applications, which are based on storing and processing large amounts of data. Given its scope, the book offers a valuable asset for all researchers, software developers, practitioners and students interested in the field of Data and Web technologies.
