computational intelligence on short-term load

GitHub

Computational Intelligence 9802 This repository contains the final projects of the course Computational Intelligence lectured at the university of Guilan in Spring 2020 This page is still being updated Note that due to the spread of the coronavirus (COVID-19) classes are held online through this link This course presents an introduction to the concepts and algorithms of Computational

S I : STOCHASTIC MODELING AND OPTIMIZATION IN

• Propositioning NARX for short-term load forecasting with the inclusion of special days In the literature computational-intelligence-based methods are usually used for long-term forecasting Only a few papers have examined the effectiveness of NARX models for short-term load forecasting and none of them applied NARX to more than

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Short-term Electric Load Forecasting Using Computational Intelligence Methods Sergio Jurado∗ Juan Peralta† Angela Nebot` ‡ Francisco Mugica and Paulo Cortez ∗Sensing Control Systems Arago 208-210 08011 Barcelona Spain Email: sergio juradosens-ingcontrol

A new approach for the short

Jan 01 2007Free Online Library: A new approach for the short-term load forecasting with autoregressive and artificial neural network models by International Journal of Computational Intelligence Research Computers and office automation Computers and Internet Electric power system protection Technology application Electric power systems Protection and preservation Turkey Neural

Computational Intelligence Approaches for Energy Load

Seyedeh Narjes Fallah Mehdi Ganjkhani Shahaboddin Shamshirband Kwok-wing Chau 2019 Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview Energies MDPI Open Access Journal vol 12(3) pages 1-21 January Ritwik Haldar Ashraf Hossain Kirtan Gopal Panda 2019

Computational Intelligence Approaches for Energy Load

Seyedeh Narjes Fallah Mehdi Ganjkhani Shahaboddin Shamshirband Kwok-wing Chau 2019 Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview Energies MDPI Open Access Journal vol 12(3) pages 1-21 January Ritwik Haldar Ashraf Hossain Kirtan Gopal Panda 2019

Application of hybrid computational intelligence models in

Jul 15 2016The treatment of bus load characteristics is held with computational intelligence techniques such as clustering and ANN Neural network based systems are a favorable scheme in recent years in price and load predictions over traditional time series models

Short

Apr 01 2015The purpose of the short-term electricity demand forecasting is to forecast in advance the system load represented by the sum of all consumers load at the same time - Short-term Power Demand Forecasting using the Differential Polynomial Neural Network JO - International Journal of Computational Intelligence Systems SP - 297 EP - 306 VL

Supplier Short Term Load Forecasting Using Support Vector

Supplier Short Term Load Forecasting Using Support Vector Regression and Exogenous Input In power systems task of load forecasting is important for keeping equilibrium between production and consumption With liberalization of electricity markets task of load forecasting changed because each market participant has to forecast their own load

Computational Intelligence Technique for Solving Fuzzy

computational intelligence dynamic system economic load dispatch emission fossil fuel fuzzy logic 1 Introduction Computing accuracy is vital to determine the best outcomes when solving economic dispatch problem [1] Precise economic dispatch requires several consideration such as generators limits

Computational Intelligence Techniques of Long

Computational Intelligence Techniques of Long-Term Electric Power Load Forecasting on Iraqi National Grid Hanan Mikhael Dawood Lecturer Younis Muhhy Nsaif College of Engineering University of Baghdad Baghdad Iraq Abstract Load forecasting has played an important role in generation transmission and distribution system planning

Computational Intelligence Technique for Solving Fuzzy

computational intelligence dynamic system economic load dispatch emission fossil fuel fuzzy logic 1 Introduction Computing accuracy is vital to determine the best outcomes when solving economic dispatch problem [1] Precise economic dispatch requires several consideration such as generators limits

Computational Intelligence Approaches for Energy Load

Seyedeh Narjes Fallah Mehdi Ganjkhani Shahaboddin Shamshirband Kwok-wing Chau 2019 Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview Energies MDPI Open Access Journal vol 12(3) pages 1-21 January Ritwik Haldar Ashraf Hossain Kirtan Gopal Panda 2019

Applied Computational Intelligence and Soft Computing

Applied Computational Intelligence and Soft Computing provides a forum for research that connects the disciplines of computer science engineering and mathematics using the technologies of computational intelligence and soft computing

Computational Intelligence Laboratory Peking University

Research Reseach Projects Swarm Intelligence We have done a lot of work on novel approaches in the filed of swarm intelligence Recently we proposed Generative Adeversarial Optimization (GAO) which combines neural network and black-box optimization And we proposed Fireworks Algorithm (FWA) in 2010 which is a powerful swarm intelligence algorithm and attracted lots of attention

Computational Intelligence Theory and Applications

Computational Intelligence Theory and Applications International Conference 9th Fuzzy Days in Dortmund Germany Sept 18-20 2006 Proceedings Short-Term Load Forecasting in Power System Using Least Squares Support Vector Machine Pages 117-126 LV Ganyun (et al )

Computational Intelligence: An Introduction: Engelbrecht

Computational Intelligence: An Introduction Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments The main focus of this text is centred on the computational modelling of biological and natural intelligent systems encompassing swarm intelligence fuzzy systems artificial neutral networks artificial

Data

We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS) Even though ML has been used as an approach to heat load prediction in literature it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific For that reason we compared and evaluated three

Deep Learning Application: Load Forecasting in Big Data of

Oct 24 2019Zheng J Xu C Zhang Z Li X : Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network In: 2017 51st Annual Conference on Information Sciences and Systems (CISS) pp 1–6 (2017) Google Scholar

Short

May 11 2019Short-term wind energy forecasting can be improved using this model to enhance the wind power quality 12 h ahead b) No previous studies applied computational intelligence for short-term wind speed forecasting for such heights in Uruguay which is a humid subtropical climate region

MODELING AND FORECASTING SHORT ERM LECTRICITY

computational intelligence methods and econometric techniques have largely dominated literature that is more recent The choice of the appropriate technique for load forecasting depends largely upon the forecast horizon Short-term load forecasts (STLF) which forecast one hour to one week ahead have

1 A Single Scalable LSTM Model for Short

At a district level [21] addresses the problem of accurate short-term load forecasting including both meteorological and technical variables The proposed model is based on a DL algorithm that combines di erent backpropagation techniques to ease its computational burden Similarly a DL

Computational Intelligence

Computational Intelligence: Principles Techniques and Applications 2005 Abstract Ghanbari A Hadavandi E and Abbasian-Naghneh S An intelligent ACO-SA approach for short term electricity load prediction Proceedings of the Advanced intelligent computing theories and applications and 6th international conference on Intelligent computing

Short

Jul 14 2011Abstract: Load Forecasting plays a critical role in the management scheduling and dispatching operations in power systems and it concerns the prediction of energy demand in different time spans In future electric grids to achieve a greater control and flexibility than in actual electric grids a reliable forecasting of load demand could help to avoid dispatch problems given by unexpected

DOAB: Directory of Open Access Books

This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer's perspective Thorough well-organised and up-to-date it examines in detail some of the important aspects of this very exciting and rapidly emerging technology including machine learning particle swarm optimization

IEEE Transactions on Emerging Topics in Computational

Bibliographic content of IEEE Transactions on Emerging Topics in Computational Intelligence Volume 3 default search action combined dblp search author search Modeling and Forecasting Short-Term Power Load With Copula Model and Deep Belief Network your browser will contact the API of unpaywall to load hyperlinks to open access

Computational Intelligence Approaches for Energy Load

the hybrid computational intelligence (CI)-based load forecasting technique The advantages and are known as short-term load forecasting (STLF) For longer time spans—ranging from a month to a few years—the phrase medium-term load forecasting (MTLF) is used and if the time cycle is from a