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| Author | : Derek W. Bunn |
| Publisher | : |
| Total Pages | : 256 |
| Release | : 1985 |
| Genre | : Business & Economics |
| ISBN | : |
Takes a practical look at how short-term forecasting has actually been undertaken and is being developed in public utility organizations.
| Author | : S.A. Soliman |
| Publisher | : Elsevier |
| Total Pages | : 440 |
| Release | : 2010-05-26 |
| Genre | : Business & Economics |
| ISBN | : 9780123815446 |
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
| Author | : Filippo Maria Bianchi |
| Publisher | : Springer |
| Total Pages | : 72 |
| Release | : 2017-11-09 |
| Genre | : Computers |
| ISBN | : 3319703382 |
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 | : H. Lee Willis |
| Publisher | : CRC Press |
| Total Pages | : 770 |
| Release | : 2002-08-09 |
| Genre | : Technology & Engineering |
| ISBN | : 9780203910764 |
Containing 12 new chapters, this second edition offers increased coverage of weather correction and normalization of forecasts, anticipation of redevelopment, determining the validity of announced developments, and minimizing risk from over- or under-planning. It provides specific examples and detailed explanations of key points to consider for both standard and unusual utility forecasting situations, information on new algorithms and concepts in forecasting, a review of forecasting pitfalls and mistakes, case studies depicting challenging forecast environments, and load models illustrating various types of demand.
| Author | : Maria Jacob |
| Publisher | : Springer Nature |
| Total Pages | : 108 |
| Release | : 2019-09-25 |
| Genre | : Mathematics |
| ISBN | : 303028669X |
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 | : Antonio Gabaldón |
| Publisher | : MDPI |
| Total Pages | : 324 |
| Release | : 2021-02-26 |
| Genre | : Technology & Engineering |
| ISBN | : 303943442X |
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 | : Abdo Abou Jaoude |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 156 |
| Release | : 2021-01-27 |
| Genre | : Computers |
| ISBN | : 1838808256 |
Mathematical probability and statistics are an attractive, thriving, and respectable part of mathematics. Some mathematicians and philosophers of science say they are the gateway to mathematics’ deepest mysteries. Moreover, mathematical statistics denotes an accumulation of mathematical discussions connected with efforts to most efficiently collect and use numerical data subject to random or deterministic variations. Currently, the concept of probability and mathematical statistics has become one of the fundamental notions of modern science and the philosophy of nature. This book is an illustration of the use of mathematics to solve specific problems in engineering, statistics, and science in general.
| Author | : Leonard Barolli |
| Publisher | : Springer |
| Total Pages | : 994 |
| Release | : 2019-06-20 |
| Genre | : Technology & Engineering |
| ISBN | : 303022354X |
This book presents scientific interactions between the three interwoven and challenging areas of research and development of future ICT-enabled applications: software, complex systems and intelligent systems. Software intensive systems heavily interact with other systems, sensors, actuators, and devices, as well as other software systems and users. More and more domains involve software intensive systems, e.g. automotive, telecommunication systems, embedded systems in general, industrial automation systems and business applications. Moreover, web services offer a new platform for enabling software intensive systems. Complex systems research focuses on understanding overall systems rather than their components. Such systems are characterized by the changing environments in which they act, and they evolve and adapt through internal and external dynamic interactions. The development of intelligent systems and agents features the use of ontologies, and their logical foundations provide a fruitful impulse for both software intensive systems and complex systems. Research in the field of intelligent systems, robotics, neuroscience, artificial intelligence, and cognitive sciences is a vital factor in the future development and innovation of software intensive and complex systems.
| Author | : Wei-Chiang Hong |
| Publisher | : MDPI |
| Total Pages | : 445 |
| Release | : 2019-01-29 |
| Genre | : |
| ISBN | : 3038975826 |
This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
| Author | : |
| Publisher | : |
| Total Pages | : 744 |
| Release | : 1988 |
| Genre | : Power resources |
| ISBN | : |
