A Comparative Analysis Of Load Forecasting Methods
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A Comparative Analysis Of Load Forecasting Methods
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Author : Golam Hossein Moazampour
language : en
Publisher:
Release Date : 1986
A Comparative Analysis Of Load Forecasting Methods written by Golam Hossein Moazampour and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with categories.
Comparative Models For Electrical Load Forecasting
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Author : Derek W. Bunn
language : en
Publisher:
Release Date : 1985
Comparative Models For Electrical Load Forecasting written by Derek W. Bunn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with Business & Economics categories.
Takes a practical look at how short-term forecasting has actually been undertaken and is being developed in public utility organizations.
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.
A Comparative Study Of Short Term Electric Vehicle Load Forecasting Using Data Driven Multivariate Probabilistic Deepar Approach
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Author : Aidin Vahidmohammadi
language : en
Publisher:
Release Date : 2021
A Comparative Study Of Short Term Electric Vehicle Load Forecasting Using Data Driven Multivariate Probabilistic Deepar Approach written by Aidin Vahidmohammadi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
With the surge of electric vehicles (EVs) and consequently the increase in power consumption, the power grid is facing many new challenges. Charging load forecasting remains one of the key challenges, that if not effectively scheduled, it may result in instability and quality-related issues in power systems. In recent years, numerous load forecasting techniques using machine learning and deep learning were proposed for predictions covering both commercial and household demands. However, there are very few studies that employed these methods to predict EV charging load behavior. This thesis proposes a multivariate RNN-based deep learning framework to forecast the short-term data-driven EV charging loads on two specific datasets for residential and workplace usage. In this research, a few popular deep learning models have been comparatively investigated to evaluate the forecasting performance of the proposed multivariate DeepAR model, a recurrent neural network-based model, as well as its univariate model on the historical charging data with exogenous variables. The 5-tuples input data used in this research include charging start time, duration of charging, charging load, time of use electricity price, and weekdays/weekends that were collected from three different locations and categorized into residential and workplace/parking lot scenarios. The short-term load forecasting algorithm in this study has been utilized multi-step daily horizons as one, three, seven and fifteens days ahead for the prediction window. Numerical results show that the multivariate DeepAR algorithm persists with manifestly higher stability and accuracy over multi-step daily prediction horizons. Its symmetric mean absolute percentage error (SMAPE) and mean absolute scaled error (MASE) are maintained at 1.9% and 4.95%, respectively, and outperform by a significant margin all other investigated deep learning and statistical models on the provided EV historical charging datasets. Eventually, the proposed framework can be further employed to formulate a more complex approach regarding charging load management at charging stations to maximize the load factor as well as balancing and flattening peak loads on the grid system.
Nature Inspired Computing Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2016-07-26
Nature Inspired Computing Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-26 with Computers categories.
As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.
Soft Computing Applications
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Author : Valentina Emilia Balas
language : en
Publisher: Springer Nature
Release Date : 2020-08-14
Soft Computing Applications written by Valentina Emilia Balas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-14 with Technology & Engineering categories.
This book presents the proceedings of the 8th International Workshop on Soft Computing Applications, SOFA 2018, held on 13–15 September 2018 in Arad, Romania. The workshop was organized by Aurel Vlaicu University of Arad, in conjunction with the Institute of Computer Science, Iasi Branch of the Romanian Academy, IEEE Romanian Section, Romanian Society of Control Engineering and Technical Informatics – Arad Section, General Association of Engineers in Romania – Arad Section and BTM Resources Arad. The papers included in these proceedings, published post-conference, cover the research including Knowledge-Based Technologies for Web Applications, Cloud Computing, Security Algorithms and Computer Networks, Business Process Management, Computational Intelligence in Education and Modelling and Applications in Textiles and many other areas related to the Soft Computing. The book is directed to professors, researchers, and graduate students in area of soft computing techniques and applications.
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
Short Term Load Forecasting 2019
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Author : Antonio Gabaldón
language : en
Publisher: MDPI
Release Date : 2021-02-26
Short Term Load Forecasting 2019 written by Antonio Gabaldón and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.
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.
Core Concepts And Methods In Load Forecasting
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Author : Stephen Haben
language : en
Publisher: Springer Nature
Release Date : 2023-06-01
Core Concepts And Methods In Load Forecasting written by Stephen Haben and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-01 with Technology & Engineering categories.
This comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code). Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital. This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.
Commercial Industrial And Household Electrical Load Modelling And Short Term Load Forecasting
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Author : Hla-U-May Marma
language : en
Publisher:
Release Date : 2020
Commercial Industrial And Household Electrical Load Modelling And Short Term Load Forecasting written by Hla-U-May Marma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
In this thesis, a transfer function-based load model is determined for commercial and industrial load. This model is derived from the composite load model which consist of an induction motor and static load. This developed model is compared to composite load model by considering two cases: 1) a small motor composition load or commercial load and 2) higher motor composition load or industrial load. The research is conducted through MATLAB/Simulink simulation. In order to compare the dynamic response of developed model, a comparative study has been done between the two models. In addition, the influence of voltage and frequency dependency terms on the overall model accuracy for developed model has been evaluated through several case studies considering both voltage and frequency dependency disturbances. A short-term load forecast model is developed for an electrically heated house. This research work is based on experimental data collected by installing current sensors in a house in St. Johns, Newfoundland, Canada. The data was collected for three years and only one-year data is used for this model. The model is based on Recurrent Neural Network (RNN) with wavelet transform. The proposed model is verified by comparing other developed models in the literature through MATLAB deep learning toolbox and wavelet toolbox. The proposed model can more accurately forecast the load.
