Short Term Forecasting Of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks With Exogenous Vector Inputs
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Short Term Forecasting Of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks With Exogenous Vector Inputs
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Author :
language : en
Publisher:
Release Date : 2017
Short Term Forecasting Of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks With Exogenous Vector Inputs written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.
Short Term Forecasting Of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks With Exogenous Multivariable Inputs
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Author : Jaime H. Buitrago
language : en
Publisher:
Release Date : 2017
Short Term Forecasting Of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks With Exogenous Multivariable Inputs written by Jaime H. Buitrago and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1\% have been achieved, which is a 30\% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. In addition, in order to improve the robustness of the forecast to variations in the number of neurons and other network parameters, the author proposes a method using an exponential decay of the error weights for training the neural network. The modification consists in giving higher error weight to more recent values and lower weight to older values of the training set. By doing this, mover recent values have a higher influence on the calculation of the synaptic weights and therefore the forecast produced by the NARX network is more accurate. This method, combined with the use of Bayesian regularization for training, results in improved forecast accuracy of up to 25\% and robustness to variation in parameter selection. The New England electrical load data are used to train and validate the forecast prediction.
Short Term Load Forecasting By Artificial Intelligent Technologies
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Author : Wei-Chiang Hong
language : en
Publisher: MDPI
Release Date : 2019-01-29
Short Term Load Forecasting By Artificial Intelligent Technologies written by Wei-Chiang Hong and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with categories.
This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Recurrent Neural Networks For Short Term Load Forecasting
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Author : Filippo Maria Bianchi
language : en
Publisher: Springer
Release Date : 2017-11-09
Recurrent Neural Networks For Short Term Load Forecasting written by Filippo Maria Bianchi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-09 with Computers categories.
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.
Advances In Artificial Intelligence For Renewable Energy Systems And Energy Autonomy
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Author : Mukhdeep Singh Manshahia
language : en
Publisher: Springer Nature
Release Date : 2023-06-14
Advances In Artificial Intelligence For Renewable Energy Systems And Energy Autonomy written by Mukhdeep Singh Manshahia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Technology & Engineering categories.
This book provides readers with emerging research that explores the theoretical and practical aspects of implementing new and innovative artificial intelligence (AI) techniques for renewable energy systems. The contributions offer broad coverage on economic and promotion policies of renewable energy and energy-efficiency technologies, the emerging fields of neuro-computational models and simulations under uncertainty (such as fuzzy-based computational models and fuzzy trace theory), evolutionary computation, metaheuristics, machine learning applications, advanced optimization, and stochastic models. This book is a pivotal reference for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in artificial intelligence, renewable energy systems, and energy autonomy.
Ai And Learning Systems
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Author : Konstantinos Kyprianidis
language : en
Publisher: BoD – Books on Demand
Release Date : 2021-02-17
Ai And Learning Systems written by Konstantinos Kyprianidis and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-17 with Technology & Engineering categories.
Over the last few years, interest in the industrial applications of AI and learning systems has surged. This book covers the recent developments and provides a broad perspective of the key challenges that characterize the field of Industry 4.0 with a focus on applications of AI. The target audience for this book includes engineers involved in automation system design, operational planning, and decision support. Computer science practitioners and industrial automation platform developers will also benefit from the timely and accurate information provided in this work. The book is organized into two main sections comprising 12 chapters overall: •Digital Platforms and Learning Systems •Industrial Applications of AI
The Internet Of Energy
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Author : Sheila Mahapatra
language : en
Publisher: CRC Press
Release Date : 2024-02-20
The Internet Of Energy written by Sheila Mahapatra and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-20 with Computers categories.
Providing innovative efficient, clean, and safe solutions and research for interfacing internet technology with energy power grids for smart cities and smart transportation, this new volume discusses the use and automation of electricity infrastructures for energy producers and manufacturers, integrating the implementation of the Internet of Things (IoT) technology for distributed energy systems in order to optimize energy efficiency and wastage. This volume offers a wide range of research on using IoT for energy solutions, such as algorithms for the design and control of energy grids, investigations of thermal efficiency from solar grids, energy for smart buildings using IoT, deep learning for electrical load forecasting, hybrid ultracapacitors in solar microgrids, induction motor-driven electric vehicles, power loss reduction and voltage improvement, and much more.
Electrical Load Forecasting
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Author : S.A. Soliman
language : en
Publisher: Elsevier
Release Date : 2010-05-26
Electrical Load Forecasting written by S.A. Soliman and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-26 with Business & Economics categories.
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
Machine Learning Image Processing Network Security And Data Sciences
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Author : Arup Bhattacharjee
language : en
Publisher: Springer Nature
Release Date : 2020-06-23
Machine Learning Image Processing Network Security And Data Sciences written by Arup Bhattacharjee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-23 with Computers categories.
This two-volume set (CCIS 1240-1241) constitutes the refereed proceedings of the Second International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2020, held in Silchar, India. Due to the COVID-19 pandemic the conference has been postponed to July 2020. The 79 full papers and 4 short papers were thoroughly reviewed and selected from 219 submissions. The papers are organized according to the following topical sections: data science and big data; image processing and computer vision; machine learning and computational intelligence; network and cyber security.
Micro Electronics And Telecommunication Engineering
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Author : Devendra Kumar Sharma
language : en
Publisher: Springer Nature
Release Date : 2020-04-02
Micro Electronics And Telecommunication Engineering written by Devendra Kumar Sharma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Technology & Engineering categories.
This book presents selected papers from the 3rd International Conference on Micro-Electronics and Telecommunication Engineering, held at SRM Institute of Science and Technology, Ghaziabad, India, on 30-31 August 2019. It covers a wide variety of topics in micro-electronics and telecommunication engineering, including micro-electronic engineering, computational remote sensing, computer science and intelligent systems, signal and image processing, and information and communication technology.
