Forecasting Electricity Load In New Jersey With Artificial Neural Networks
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Forecasting Electricity Load In New Jersey With Artificial Neural Networks
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Author : Erik W. Raab
language : en
Publisher:
Release Date : 2022
Forecasting Electricity Load In New Jersey With Artificial Neural Networks written by Erik W. Raab and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Electric power-plants categories.
Load forecasting is an important tool for both the energy and environmental sectors. It has progressed hand-in-hand with machine learning innovation, where recurrent neural networks, a type of artificial neural network, is primarily used. This thesis compares progressively complex, feed-forward artificial neural networks using a mix of weather and temporal data. We demonstrate that electrical load in New Jersey can be reliably predicted using memory-less algorithms with minimal predictors drawn from preexisting public data sources. The methods used in this thesis could be used to build competitive load forecasting models in other states, and if included in diverse model ensembles, may generate significant improvements.
Computational Intelligence In Power Engineering
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Author : Bijaya Ketan Panigrahi
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-20
Computational Intelligence In Power Engineering written by Bijaya Ketan Panigrahi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-20 with Computers categories.
This volume deals with different computational intelligence (CI) techniques for solving real world power industry problems. It will be extremely helpful for the researchers as well as the practicing engineers in the power industry.
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
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
Artificial Higher Order Neural Networks For Economics And Business
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Author : Zhang, Ming
language : en
Publisher: IGI Global
Release Date : 2008-07-31
Artificial Higher Order Neural Networks For Economics And Business written by Zhang, Ming and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-31 with Computers categories.
"This book is the first book to provide opportunities for millions working in economics, accounting, finance and other business areas education on HONNs, the ease of their usage, and directions on how to obtain more accurate application results. It provides significant, informative advancements in the subject and introduces the HONN group models and adaptive HONNs"--Provided by publisher.
Forecasting Long Term Electricity Demand Time Series Using Artificial Neural Networks
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Author : Christian Behm
language : en
Publisher:
Release Date : 2020
Forecasting Long Term Electricity Demand Time Series Using Artificial Neural Networks written by Christian Behm 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.
Accurate forecast of electricity load are increasingly important. We present a method to forecast long-term weather-dependent hourly electricity load using artificial neural networks. The fully connected dense artificial neural networks with 5 hidden layers and 1,024 hidden nodes per layer are trained using historic data from 2006 to 2015. Input parameters comprise calendrical information, annual peak loads and weather data. The results are benchmarked against the method to forecast electric loads used in the current mid-term adequacy forecasts published by the European Network of Transmission System Operators (entso-e). For validation year 2016, our approach shows a mean absolute percentage error of 2.8%, whereas the common approach as used by entso-e shows an average error of 4.8% using peak load scaling. Further, we conduct forecasts for Germany, Sweden, Spain, and France for scenario year 2025 and assess parameter variations. Our approach can serve to increase prediction accuracy of future electricity loads.
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.
Electric Load Forecasting Using An Artificial Neural Networks
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Author : Natalia Gotman
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2014-03
Electric Load Forecasting Using An Artificial Neural Networks written by Natalia Gotman and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03 with categories.
Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day - operating forecasting; one-day-week-month-ahead load forecasting - short-term load forecasting; one-month-quarter-year-ahead load forecasting - long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.
Recurrent Neural Networks For Temporal Data Processing
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Author : Hubert Cardot
language : en
Publisher: BoD – Books on Demand
Release Date : 2011-02-09
Recurrent Neural Networks For Temporal Data Processing written by Hubert Cardot 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 2011-02-09 with Computers categories.
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.
Ecml Pkdd 2020 Workshops
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Author : Irena Koprinska
language : en
Publisher: Springer Nature
Release Date : 2021-02-01
Ecml Pkdd 2020 Workshops written by Irena Koprinska and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-01 with Computers categories.
This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.
