Forecasting

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Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. (Wikipedia.org)






Conferences related to Forecasting

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2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


Oceans 2020 MTS/IEEE GULF COAST

To promote awareness, understanding, advancement and application of ocean engineering and marine technology. This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.

  • OCEANS 2018 MTS/IEEE Charleston

    Ocean, coastal, and atmospheric science and technology advances and applications

  • OCEANS 2017 - Anchorage

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorials on ocean technology, workshops and town meetings on policy and governmental process.

  • OCEANS 2016

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor a joint annual conference and exposition on ocean science, engineering, and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 500 technical papers and 150 -200 exhibits.

  • OCEANS 2015

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor a joint annual conference and exposition on ocean science, engineering, and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 150-200 exhibits.

  • OCEANS 2014

    The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 150-200 exhibits.

  • OCEANS 2013

    Three days of 8-10 tracks of technical sessions (400-450 papers) and concurent exhibition (150-250 exhibitors)

  • OCEANS 2012

    Ocean related technology. Tutorials and three days of technical sessions and exhibits. 8-12 parallel technical tracks.

  • OCEANS 2011

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2010

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2009

  • OCEANS 2008

    The Marine Technology Society (MTS) and the Oceanic Engineering Society (OES) of the Institute of Electrical and Electronic Engineers (IEEE) cosponsor a joint conference and exposition on ocean science, engineering, education, and policy. Held annually in the fall, it has become a focal point for the ocean and marine community to meet, learn, and exhibit products and services. The conference includes technical sessions, workshops, student poster sessions, job fairs, tutorials and a large exhibit.

  • OCEANS 2007

  • OCEANS 2006

  • OCEANS 2005

  • OCEANS 2004

  • OCEANS 2003

  • OCEANS 2002

  • OCEANS 2001

  • OCEANS 2000

  • OCEANS '99

  • OCEANS '98

  • OCEANS '97

  • OCEANS '96


2019 IEEE Industry Applications Society Annual Meeting

The Annual Meeting is a gathering of experts who work and conduct research in the industrial applications of electrical systems.


2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.


2019 IEEE Milan PowerTech

PowerTech is the IEEE PES anchor conference in Europe and has been attended by hundreds of delegates from around the world. It will be an international forum with programme for individuals working in industry and academia, to network, exchange ideas, and discuss the results of their research and development work.

  • 2017 IEEE Manchester PowerTech

    this is IEEE PES anchor conference in Europe covering all areas of electrical power engineering

  • 2015 IEEE Eindhoven PowerTech

    This conference will continue the tradition of the PowerTech conferences held in odd years in Athens, Stockholm, Budapest, Porto, Bologna, St. Petersburg, Lausanne, Bucharest, Trondheim and Grenoble.PowerTech is the anchor conference of the IEEE Power Engineering Society in Europe. It is intended to provide a forum, in the European geographical area, for scientists and engineers interested in electric power engineering to exchange ideas, results of their scientific work, to learn from each other as well as to establish new friendships and rekindle existing ones. Student participation in Power Tech provides an important ingredient toward the event’s success: a special award, the Basil Papadias Award, is presented to the author of the best student paper at each edition. The Power Engineering Society of IEEE organized similar conferences in other parts of the world, such as PowerCon, in the Asia-Pacific region.

  • 2013 IEEE Grenoble PowerTech

    PowerTech is the anchor conference of the IEEE Power & Energy Society in Europe. It is intended to provide a forum for electric power engineering scientists and engineers to share ideas, results of their scientific work, to learn from each other as well as to establish new friendships and maintain existing ones.

  • 2011 IEEE Trondheim PowerTech

    PowerTech is the anchor conference of the IEEE Power & Energy Society in Europe. It is intended to provide a forum for electric power engineering scientists and engineers to share ideas, results of their scientific work and to learn from each other.

  • 2009 IEEE Bucharest Power Tech

    PowerTech is the anchor conference of the IEEE-PES in Europe. It is intended to provide a forum for scientists and engineers interested in electric power engineering to share ideas, results of their scientific work, to learn from each other as well as to establish new friendships and rekindle existing ones.

  • 2007 IEEE Power Tech

  • 2005 IEEE Russia Power Tech

  • 2003 Bologna Power Tech


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Periodicals related to Forecasting

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


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Most published Xplore authors for Forecasting

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Xplore Articles related to Forecasting

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Density Forecasting for Long-Term Peak Electricity Demand

[{u'author_order': 1, u'affiliation': u'Business and Economic Forecasting Unit, Monash University, Clayton, Australia', u'authorUrl': u'https://ieeexplore.ieee.org/author/37683888200', u'full_name': u'Rob J. Hyndman', u'id': 37683888200}, {u'author_order': 2, u'affiliation': u'Business and Economic Forecasting Unit, Monash University, Clayton, Australia', u'authorUrl': u'https://ieeexplore.ieee.org/author/37309262200', u'full_name': u'Shu Fan', u'id': 37309262200}] IEEE Transactions on Power Systems, 2010

Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long-term context, planners must adopt a probabilistic view of potential peak demand levels. Therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for ...


Wind Speed Forecasting Based on Support Vector Machine with Forecasting Error Estimation

[{u'author_order': 1, u'affiliation': u'School of Energy and Power Engineering, North China Electric Power University, Beijing 102206, China. E-MAIL: jiguorui@gmail.com', u'authorUrl': u'https://ieeexplore.ieee.org/author/37702076500', u'full_name': u'Guo-Rui Ji', u'id': 37702076500}, {u'author_order': 2, u'affiliation': u'School of Control Science and Engineering, North China Electric Power University, Baoding 071003, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37273434600', u'full_name': u'Pu Han', u'id': 37273434600}, {u'author_order': 3, u'affiliation': u'School of Control Science and Engineering, North China Electric Power University, Baoding 071003, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37278658600', u'full_name': u'Yong-Jie Zhai', u'id': 37278658600}] 2007 International Conference on Machine Learning and Cybernetics, 2007

An approach of a mean hourly wind speed forecasting in wind farm is proposed in this paper. It applies support vector regression as well as forecasting error estimation. Firstly, support vector regression is applied to the mean hourly wind speed forecasting. Secondly, a support vector classifier is trained to estimate the forecasting error. Finally, the forecasting results can tailor themselves ...


The short-term electric load forecasting grid model based on MDRBR algorithm

[{u'author_order': 1, u'affiliation': u'North China Electr. Power Univ., Baoding, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37538674300', u'full_name': u'Ran Li', u'id': 37538674300}, {u'author_order': 2, u'affiliation': u'North China Electr. Power Univ., Baoding, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37598856000', u'full_name': u'Jing Hua Li', u'id': 37598856000}, {u'author_order': 3, u'affiliation': u'North China Electr. Power Univ., Baoding, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37067578000', u'full_name': u'He Ming Li', u'id': 37067578000}] 2006 IEEE Power Engineering Society General Meeting, 2006

In load forecasting of a bulk power system, the geographical scope of forecasting region is large, the main electrical load effecting factors in sub districts are different greatly. So it is very significance to establish different load forecasting model according to self-feature of each sub district of a large area, by which the forecasting load is closer to the fact ...


Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting

[{u'author_order': 1, u'affiliation': u'State Key Lab of Power Systems, Dept. of Electrical Engineering, Tsinghua University, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/38508820300', u'full_name': u'Yang Wang', u'id': 38508820300}, {u'author_order': 2, u'affiliation': u'State Key Lab of Power Systems, Dept. of Electrical Engineering, Tsinghua University, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37338542200', u'full_name': u'Qing Xia', u'id': 37338542200}, {u'author_order': 3, u'affiliation': u'State Key Lab of Power Systems, Dept. of Electrical Engineering, Tsinghua University, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37334342300', u'full_name': u'Chongqing Kang', u'id': 37334342300}] IEEE Transactions on Power Systems, 2011

Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative ...


Development of 24-hour optimal scheduling algorithm for energy storage system using load forecasting and renewable energy forecasting

[{u'author_order': 1, u'affiliation': u'Division of Energy Systems Research, Ajou University, Suwon, South Korea', u'full_name': u'Wonjun Lee'}, {u'author_order': 2, u'affiliation': u'Division of Energy Systems Research, Ajou University, Suwon, South Korea', u'full_name': u'Jaesung Jung'}, {u'author_order': 3, u'affiliation': u'Department of Energy Science, Sungkyunkwan University, Suwon, South Korea', u'full_name': u'Munsu Lee'}] 2017 IEEE Power & Energy Society General Meeting, 2017

This paper presents the 24-hour optimal scheduling algorithm for Energy Storage System (ESS) using load forecasting and renewable energy forecasting in South Korea electricity tariff structure. For load forecasting and renewable energy forecasting, 24-hour multivariate forecasting model combining very-short-term and short-term forecasting models is developed. Then, load and renewable forecasts are input to the optimal ESS scheduling algorithm. The objective ...


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Educational Resources on Forecasting

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eLearning

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IEEE-USA E-Books

  • On Replenishment Rules, Forecasting and the Bullwhip Effect in Supply Chains

    On Replenishment Rules, Forecasting and the Bullwhip Effect in Supply Chains focuses on supply chain co-ordination. The bullwhip effect is used as the key example of supply chain inefficiency. The authors focus both on the managerial relevance of the bullwhip effect and the methodological issues making it essential reading for both managers and researchers.

  • A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models

    This chapter presents an inductive learning procedure that combines several techniques to generate a fully data‐driven forecasting model. It considers a forecasting method based on appropriate techniques for controlling Artificial neural networks (ANNs) complexity with simultaneous selection of explanatory input variables via a combination of filter and wrapper techniques. Input selection is performed, without user intervention, by applying Chaos theory and Bayesian inference. The challenging problem of rainfall forecasting is employed for showing the robustness of the proposed technique in dealing with different time‐series dynamics. In nonlinear chaotic time‐series analysis, local models are developed via the application of an automatic clustering algorithm based on the rival penalized expectation‐maximization (RPEM) algorithm. Neural network models are estimated, without cross‐validation, relying on data partitioning and Bayesian regularization for complexity control. The proposed forecasting model has been successfully tested using rainfall data from six major hydrographic basins in Brazil.

  • Univariate methods for short-term load forecasting

    This chapter validates the study on reviewing methods for short‐term load forecasting and using two intraday load time series to compare a variety of univariate methods. It argues that a double seasonal version of Holt‐Winters exponential smoothing was the most accurate method, with a new approach based on principal component analysis (PCA) also performing well. By investigating the performance of a range of univariate methods over different forecast horizons, it is possible to ascertain the value of employing multivariate information such as weather variables. The chapter evaluates post‐sample forecasting performance from the various methods using the mean absolute percentage error (MAPE) and the mean absolute error (MAE). It presents autoregressive moving average (ARMA) and exponential smoothing methods designed in order to capture the intraday and intraweek seasonal cycles evident in our 30‐week samples of intraday load data.

  • Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems

    This chapter describes a forecasting methodology based on the Weighted nearest neighbors (WNNs) techniques. This technique provides a very simple approach to forecast power system variables characterized by daily and weekly repetitive patterns, such as energy demand and prices. Three case studies are used in the chapter to illustrate the potential of the WNN method: the hourly energy demand in the Spanish power system; the hourly marginal prices of the day‐ahead Spanish electricity market; and the hourly demand of a particular customer. Recently, data mining techniques based on the k‐nearest neighbors (kNN) method have been applied to the next‐day load forecasting problem. In the last few years, machine learning techniques, such as artificial neural networks (ANNs), have been applied to energy price prediction owing to their relatively good performance in load forecasting and load pattern recognition. The chapter computes some of the prediction errors to assess the performance of the WNN and competing forecasting methodologies.

  • Short-term forecasting of electricity prices using mixed models

    This chapter focuses on short-term forecasting and offers a simple but accurate method and an automatic tool to compute 1-day-ahead forecasts of electricity prices. It introduces two new models, each of which deals with the 24-hourly time series of electricity prices instead of a complete one. The chapter obtains the appropriate length of the time series used to build the forecasting autoregressive integrated moving average (ARIMA) models. It presents some previous work on short-term forecasting of electricity prices and reviews the theoretical basics of time series analysis. The chapter considers a study of forecasting errors from two different points of view: descriptive and applying locally weighted regression (LOESS). It also presents the fundamentals of the design of experiments (DOE) as well as its application to develop mixed models, selecting the preferred combination of "Model" and "Length" in terms of prediction error.

  • Introduction

    This introduction provides an overview of the key concepts discussed in the subsequent chapters of this book. The book deals with advances in forecasting technologies in electric power system applications. In addition to power system load forecasting, it discusses electricity price forecasting and a storm‐caused outage duration forecasting. The book offers sophisticated treatments of load forecasting, while deals with innovative approaches to price forecasting. It describes five methods: autoregressive moving average (ARMA) modeling; periodic AR modeling; exponential smoothing for double seasonality; a recently proposed alternative exponential smoothing formulation; and a method based on principal component analysis (PCA) of the daily load profiles. The book summarizes a set of stochastic process models that can be used for electricity prices according to the purpose of modeling. It also describes commonly used continuous time stochastic models such as Brownian motion, mean reversion process, geometric Brownian motion, geometric mean reversion process.

  • Electricity Price Forecasting Using Neural Networks and Similar Days

    This chapter focuses on day‐ahead forecasts of electricity price in the PJM market using artificial neural network (ANN) model based on the similar days (SD) method. The PJM competitive market is a regional transmission organization (RTO) that plays a vital role in the US electric system. The chapter contributes to forecast electricity prices in the day‐ahead market. In addition to the integration of SD and ANN method, it also proposes a new technique to forecast hourly electricity prices in the PJM market using a recursive neural network (RNN), which is based on the SD method. The proposed RNN model is also applied to generate the next three‐day price forecasts. To evaluate the performance of the proposed neural networks, the mean absolute percentage error (MAPE), mean absolute error (MAE), and forecast mean square error (FMSE) are calculated.

  • Load Forecasting

    Load forecast plays a crucial role in all aspects of planning, operation and control of an electric power system. It is an essential function for operating a power network reliably and economically. Load forecast can be classified as short term load forecast (STLF), midterm load forecast (MTLF), and long‐term load forecast (LTLF). Forecasting methodologies can be classified on the method used. In a more definitive way, they can be categorized as deterministic or probabilistic. Spatial load forecasting is based on three aspects as main requirements: how well the forecast supports the planning process, where will the future load develop, and when will the expected load growth occur. The three aspects are explained to indicate their effect on estimating the load forecast and consequently on the planning process. Spatial electric load forecasting methods can be grouped into three categories: nonanalytic, trending and simulation.

  • ShortTerm Load Forecasting

    The principal objective of short-term load forecasting (STLF) is to provide the load prediction for basic generation scheduling functions, for assessing the security of system operation, and for timely dispatcher information. This chapter presents a discussion of STLF.

  • Electricity Price Forecasting

    With the introduction of deregulation into the power industry, the price of electricity has been the key to all activities in the power market. Accurately and efficiently forecasting electricity prices becomes more and more important. In this chapter, the authors mainly describe short-term price forecasting (STPF) of electricity in the environment of a restructured power market.



Standards related to Forecasting

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No standards are currently tagged "Forecasting"