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Author: Filippo Maria Bianchi
Publisher: Springer
ISBN: 3319703382
Category : Computers
Languages : en
Pages : 74
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Book Description
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Author: Michael Firth
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 164
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Book Description
Author: Filippo Maria Bianchi
Publisher: Springer
ISBN: 3319703382
Category : Computers
Languages : en
Pages : 74
Get Book Here
Book Description
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Author: Antonio Gabaldón
Publisher: MDPI
ISBN: 303943442X
Category : Technology & Engineering
Languages : en
Pages : 324
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Book Description
Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
Author: Rob J Hyndman
Publisher: OTexts
ISBN: 0987507117
Category : Business & Economics
Languages : en
Pages : 380
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Book Description
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.
Author: Mohamed E. El-Hawary
Publisher: John Wiley & Sons
ISBN: 1118171349
Category : Technology & Engineering
Languages : en
Pages : 341
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Book Description
A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial arenas. Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every maximization strategy. This book fills a gap in the literature on this increasingly important topic. Following an introductory chapter offering background information necessary for a full understanding of the forecasting issues covered, this book: Introduces advanced methods of time series forecasting, as well as neural networks Provides in-depth coverage of state-of-the-art power system load forecasting and electricity price forecasting Addresses river flow forecasting based on autonomous neural network models Deals with price forecasting in a competitive market Includes estimation of post-storm restoration times for electric power distribution systems Features contributions from world-renowned experts sharing their insights and expertise in a series of self-contained chapters Advances in Electric Power and Energy Systems is a valuable resource for practicing engineers, regulators, planners, and consultants working in or concerned with the electric power industry. It is also a must read for senior undergraduates, graduate students, and researchers involved in power system planning and operation.
Author: Maximo Camacho
Publisher:
ISBN: 9781601987426
Category : Business & Economics
Languages : en
Pages : 74
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Book Description
Short-term Forecasting for Empirical Economists seeks to close the gap between research and applied short-term forecasting. The authors review some of the key theoretical results and empirical findings in the recent literature on short-term forecasting, and translate these findings into economically meaningful techniques to facilitate their widespread application to compute short-term forecasts in economics, and to monitor the ongoing business cycle developments in real time.
Author: Alan R. Bohne
Publisher:
ISBN:
Category : Cloud forecasting
Languages : en
Pages : 106
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Book Description
A methodology for real-time operations has been developed for the short-term forecasting of cloud and precipitation fields. Pattern recognition techniques are employed to extract useful features from the data field and extrapolation techniques are used to project these features into the future. To reduce computational load, contours defined by directional codes are used to delineate features. These contours are subdivided and attributes such as length, location, and location of each segment are determined. Segment matching is performed for successive observations and attribute changes are monitored over time. Several techniques for the forecasting of attributes have been explored, and an exponential smoothing filter and a linear trend adaptive smoothing filter have been chosen as most appropriate. Currently analysis is performed on a minicomputer and image processor system utilizing radar reflectivity data. Refinement of these techniques and extension into a more comprehensive short term forecasting program is planned.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309171822
Category : Medical
Languages : en
Pages : 104
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Book Description
This report is the summary of a workshop conducted by the National Research Council in order to learn from both forecast makers and forecast users about improvements that can be made in understanding the markets for doctoral scientists and engineers. The workshop commissioned papers examined (1) the history and problems with models of demand and supply for scientists and engineers, (2) objectives and approaches to forecasting models, (3) margins of adjustment that have been neglected in models, especially substitution and quality, (4) the presentation of uncertainty, and (5) whether these forecasts of supply and demand are worthwhile, given all their shortcomings. The focus of the report was to provide guidance to the NSF and to scholars in this area on how models and the forecasts derived from them might be improved, and what role NSF should play in their improvement. In addition, the report examined issues of reporting forecasts to policymakers.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309454158
Category : Science
Languages : en
Pages : 135
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Book Description
Volcanic eruptions are common, with more than 50 volcanic eruptions in the United States alone in the past 31 years. These eruptions can have devastating economic and social consequences, even at great distances from the volcano. Fortunately many eruptions are preceded by unrest that can be detected using ground, airborne, and spaceborne instruments. Data from these instruments, combined with basic understanding of how volcanoes work, form the basis for forecasting eruptionsâ€"where, when, how big, how long, and the consequences. Accurate forecasts of the likelihood and magnitude of an eruption in a specified timeframe are rooted in a scientific understanding of the processes that govern the storage, ascent, and eruption of magma. Yet our understanding of volcanic systems is incomplete and biased by the limited number of volcanoes and eruption styles observed with advanced instrumentation. Volcanic Eruptions and Their Repose, Unrest, Precursors, and Timing identifies key science questions, research and observation priorities, and approaches for building a volcano science community capable of tackling them. This report presents goals for making major advances in volcano science.
Author: Maria Jacob
Publisher: Springer Nature
ISBN: 303028669X
Category : Mathematics
Languages : en
Pages : 108
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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.
