scholarly journals A new hybridization filtering-based linear-nonlinear models for time series forecasting

2021 ◽  
Author(s):  
Mehrnaz Ahmadi ◽  
◽  
Mehdi Khashei ◽  
Keyword(s):  
Hybrid Methods ◽  
Nonlinear Models ◽  
Prediction Methods ◽  
Short Term ◽  
Forecasting Methods ◽  
Primary Relationships ◽  
Time Dependencies ◽  
Physical Laws ◽  
Face Time ◽  
Physical Phenomena

In recent years, the idea of using a mathematical model to describe the behavior of physical phenomena has been very much considered. Specifically, a definitive model, based on physical laws, enables researchers to calculate the number of time dependencies precisely at any moment in time. However, in the real world, we often face time-dependent phenomena with many unknown or unavailable factors (Lindley, 2010; Roulston et al., 2003). In this case, when it is not possible to achieve a definite - model, the prediction methods are wide used, especially when the past observations of a variable and primary relationships between specific observations are available. Forecasting methods that are used in different fields of science can be categorized based on various aspects. For example, the prediction methods used in the field of wind energy can be divided into four categories of 1) ultra short term (several seconds to four hours), 2) short term (4 to 24 hours), 3) medium-term (1 to 7 days), and 4) long term (more than 7 days) (Zack, 2003; Soman et al., 2010). Also, the structure of forecasting methods can be divided into two types of 1) single methods and 2) hybrid methods. Each of these categories can also be subdivided into smaller subgroups.

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

A Summary of Short-Term Traffic Flow Forecasting Methods

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Gang Chang ◽  
Yi Zhang ◽  
Danya Yao ◽  
Yun Yue
Keyword(s):  
Traffic Flow ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Methods

Short-term prognosis in acute myocardial infarction: Evaluation of different prediction methods

1984 ◽  
Vol 107 (6) ◽  
pp. 1241-1251 ◽  
Author(s):  
Erling Birk Madsen ◽  
Elizabeth Gilpin ◽  
Hartmut Henning
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Methods ◽  
Short Term

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

The REPAS Study: Reliability Evaluation of Passive Safety Systems

2002 ◽  
Author(s):  
M. E. Ricotti ◽  
F. Bianchi ◽  
L. Burgazzi ◽  
F. D’Auria ◽  
G. Galassi
Keyword(s):  
Initial Conditions ◽  
Natural Circulation ◽  
Quantitative Information ◽  
External Input ◽  
Passive System ◽  
Two Phase ◽  
Passive Safety Systems ◽  
Safety Systems ◽  
Physical Laws ◽  
Physical Phenomena

The strategy of approach to the problem moves from the consideration that a passive system should be theoretically more reliable than an active one. In fact it does not need any external input or energy to operate and it relies only upon natural physical laws (e.g. gravity, natural circulation, internally stored energy, etc.) and/or “intelligent” use of the energy inherently available in the system (e.g. chemical reaction, decay heat, etc.). Nevertheless the passive system may fail its mission not only as a consequence of classical mechanical failure of components, but also for deviation from the expected behaviour, due to physical phenomena mainly related to thermalhydraulics or due to different boundary and initial conditions. The main sources of physical failure are identified and a probability of occurrence is assigned. The reliability analysis is performed on a passive system which operates in two-phase, natural circulation. The selected system is a loop including a heat source and a heat sink where the condensation occurs. The system behavior under different configurations has been simulated via best-estimate code (Relap5 mod3.2). The results are shown and can be treated in such a way to give qualitative and quantitative information on the system reliability. Main routes of development of the methodology are also depicted.

scholarly journals USE OF MOBILE DEVICE SENSORS TO CONDUCT THE PHYSICAL EXPERIMENT

2019 ◽  
pp. 345-354
Author(s):  
Sergii Tereschuk ◽  
Vira Kolmakova
Keyword(s):  
Stem Education ◽  
Mobile Device ◽  
Mobile Application ◽  
Positive Impact ◽  
Physical Experiment ◽  
Science Journal ◽  
Conservation Of Energy ◽  
Physical Experiments ◽  
Physical Laws ◽  
Physical Phenomena

The concept of "sensor" in the system of physical experiment at school is considered in the article. The possibility of using sensors in physics lessons is substantiated: transformation of an input signal into an output is accompanied by transformation of one type of energy into another (according to the law of conservation of energy), and the functioning of the sensors are based on physical phenomena (physical effects or principles), which are described by the relevant physical laws. The article deals with the methodical aspects of using the Google Science Journal mobile application in physics lessons. This application allows you to use the sensors of your mobile device for a physical experiment. As an example we consider the frontal laboratory work "Determination of the period of oscillation of the mathematical pendulum". The method of its carrying out is offered in two approaches: the first one involves the traditional technique of conducting the experiment, and the second approach is using the mobile application Google Science Journal. The article shows that the use of smartphone sensors in physics lessons has perspectives in the context of STEM education. Thus, the use of the considered application is of current importance and requires further scientific and methodological research on its use in the high school physical experimentation system. The Science Journal mobile application can be used to connect external sensors, which will have a positive impact on the introduction of STEM education, and to use Arduino in the demonstration of physical experiments by a physics teacher. Connecting sensors using an Arduino microcontroller is particularly promising in creative lab work on physics.

scholarly journals J. David Jackson (January 19, 1925–May 20, 2016): A Biographical Memoir

2021 ◽  
Vol 71 (1) ◽  
pp. 23-36
Author(s):  
Robert N. Cahn
Keyword(s):  
Civil Liberties ◽  
The United States ◽  
Classical Electrodynamics ◽  
Muon Catalyzed Fusion ◽  
Particle Collisions ◽  
Superconducting Super Collider ◽  
Theoretical Physicist ◽  
Andrei Sakharov ◽  
Physical Laws ◽  
Physical Phenomena

John David (“Dave”) Jackson, a Canadian-born theoretical physicist, contributed significantly to particle, nuclear, and atomic physics. He is best known, however, for his text Classical Electrodynamics, which has been a fixture in physics graduate education around the world for more than 50 years. It is generally referred to simply as “Jackson.” This textbook, which has inspired fear and wonder alike in generations of students, clearly reflects the author's fascination with physical phenomena, his renowned mathematical dexterity, and his appreciation of the elegance of physical laws. Jackson's major contributions to research included the theory of muon-catalyzed fusion; the analysis, with Kurt Gottfried, of angular distributions in quasi-two-body elementary particle collisions; and the elucidation of charmonium-state decays. Jackson influenced the development of physics research throughout the United States as well as internationally—particularly through his work on the nascent Superconducting Super Collider. An active promoter of civil liberties and human rights, he was one of the leaders of the efforts to free Andrei Sakharov, Yuri Orlov, and Anatoly Shcharansky from Soviet imprisonment.

Comparison of short-term solar irradiance forecasting methods when weather conditions are complicated

2018 ◽  
Vol 10 (5) ◽  
pp. 053501 ◽  
Author(s):  
Yunjun Yu ◽  
Junfei Cao ◽  
Xiaofeng Wan ◽  
Fanpeng Zeng ◽  
Jianbo Xin ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Methods

Introduction

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Physical System ◽  
Weather Prediction ◽  
Climate Prediction ◽  
Lead Times ◽  
Prediction Methods ◽  
Data Sets ◽  
Short Term ◽  
Global Data Sets ◽  
Geophysical System

This is first and foremost a book about short-term climate prediction. The predictions we have in mind are for weather/climate elements, mainly temperature (T) and precipitation (P), at lead times longer than two weeks, beyond the realm of detailed Numerical Weather Prediction (NWP), i.e. predictions for the next month and the next seasons out to at most a few years. call this short-term climate so as to distinguish it from long-term climate change which is not the main subject of this book. A few decades ago “short-term climate prediction” was known as “longrange weather prediction”. In order to understand short-term climate predictions, their skill and what they reveal about the atmosphere, ocean and land, several chapters are devoted to constructing prediction methods. The approach taken is mainly empirical, which means literally that it is based in experience. We will use global data sets to represent the climate and weather humanity experienced (and measured!) in the past several decades. The idea is to use these existing data sets in order to construct prediction methods. In doing so we want to acknowledge that every measurement (with error bars) is a monument about the workings of Nature. We thought about using the word “statistical” instead of “empirical” in the title of the book. These two notions overlap, obviously, but we prefer the word “empirical” because we are driven more by intuition than by a desire to apply existing or developing new statistical theory. While constructing prediction methods we want to discover to the greatest extent possible how the physical system works from observations. While not mentioned in the title, diagnostics of the physical system will thus be an important part of the book as well. We use a variety of classical tools to diagnose the geophysical system. Some of these tools have been developed further and/or old tools are applied in novel ways. We do not intend to cover all diagnostics methods, only those that relate closely to prediction. There will be an emphasis on methods used in operational prediction. It is quite difficult to gain a comprehensive idea from existing literature about methods used in operational short-term climate prediction.

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