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Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network
2015
Mathematical Problems in Engineering
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Then this paper designed a forecasting model based on the chaos theory and RBF neural network. ...
It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. ...
Acknowledgment This paper is supported by the National Science Foundation of China (Grant nos. 71071052 and 71471059). ...
doi:10.1155/2015/231765
fatcat:s2yforyryrgjvfqy5guhwffoua
Annual Power Load Forecasting Using Support Vector Regression Machines: A Study On Guangdong Province Of China 1985-2008
2010
Zenodo
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The performance of the new model is evaluated with a real-world dataset, and compared with two neural networks and some traditional forecasting techniques. ...
A novel approach based on support vector machines is proposed in this paper for annual power load forecasting. Different kernel functions are selected to construct a combinatorial algorithm. ...
Depending on different time horizons, load forecasting can be generally divided into short-term and mid-long-term categories. ...
doi:10.5281/zenodo.1328571
fatcat:3chwav2ipzeclcrlbhyen4byhu
Short-term Road Speed Forecasting based on Hybrid RBF Neural Network with the Aid of Fuzzy System-based Techniques in Urban Traffic Flow
2020
IEEE Access
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The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting ...
Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted ...
SHORT-TERM ROAD SPEED FORECASTING BASED ON HYBRID RBF NEURAL NETWORK A. ...
doi:10.1109/access.2020.2986278
fatcat:ttcaufas25bvzmnrpx6obfk4m4
Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
2022
Symmetry
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY ...
The self-adaptive chaotic BPNN and parallel chaos algorithm reported in [128] , and [118] , respectively, are used for forecasting the short-term electrical power load in the China network. ...
minimum redundancy based BPNN-LS-SVM [156] , and short-and medium-term load in the Xi'an power grid corporation, China. ...
doi:10.3390/sym14050955
dblp:journals/symmetry/RamadeviB22
fatcat:3oa3go7rdzdurjl4yxcivjsbf4
A survey of research progress and hot front of natural gas load forecasting from technical perspective
2020
IEEE Access
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As an important part of natural gas industry planning, load forecasting plays a vital role in the optimal dispatching and operation of the natural gas network. ...
From the perspective of prediction technology, this paper selects the literature related to natural gas load prediction from the Web of Science and CNKI database as the research object. ...
The authors are grateful for the help in writing this article by Yu Zhao and Hongbin Dai. They also thank the editor and the anonymous reviewers for their valuable comments. ...
doi:10.1109/access.2020.3044052
fatcat:635dluiv5rg3dgyo3fvdqyyxzq
Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review
2016
International Journal of Applied Information Systems
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And it is established that Neural Network such as BPN, RBF is best appropriate to be predicted chaotic behavior of climate variables like rainfall, rainfall runoff, and have efficient enough for prediction ...
To be familiar with appropriateness of Neural Network in climate prediction and spatial interpolation, e comprehensive literature review of past 50 years is done and offered in this paper. ...
As load forecasting is an important prediction aspect for industrial sectors all over the world, Mohsen Hayati, and Yazdan Shirvany, 2007, have put in an approach for short term load forecasting (STLF) ...
doi:10.5120/ijais2016451552
fatcat:53igsqqazzbipacaece54gko64
A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis
2017
Energies
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It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting. ...
The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. ...
Author Contributions: In this research activity, all the authors were involved in the data collection and preprocessing phase, model constructing, empirical research, results analysis and discussion, and ...
doi:10.3390/en10030408
fatcat:4qzwtdlr6ne7hi2iflshlohudm
Hybrid CSA optimization with seasonal RVR in traffic flow forecasting
2017
KSII Transactions on Internet and Information Systems
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Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. ...
Therefore, it is one of the most important components in the research of urban traffic scheduling. ...
They also proposed an ARIMA model based on jump in order to improve the forecasting accuracy of short-term traffic flow. ...
doi:10.3837/tiis.2017.10.011
fatcat:ses2ki3pizfgtmyebmqijcoryu
Regularization methods for the short-term forecasting of the Italian electric load
[article]
2021
arXiv
pre-print
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In fact, the aggregated forecasts yielded further relevant drops in terms of quarter-hourly and daily mean absolute percentage error, mean absolute error and root mean square error (up to 30%) over the ...
The 96x96 matrix weights form a 96x96 matrix, that can be seen and displayed as a surface sampled on a square domain. ...
Yang, Rbf neural
network and anfis-based short-term load forecasting approach in real-time
price environment, IEEE Transactions on power systems 23 (3) (2008) 853–
858.
450 [ ...
arXiv:2112.04604v1
fatcat:w3h5d4qnmzcjnc3afqiflv364q
A New Model to Short-Term Power Load Forecasting Combining Chaotic Time Series and SVM
2009
2009 First Asian Conference on Intelligent Information and Database Systems
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Findings show that the model is effective and highly accurate in the forecasting of short-term power load. ...
According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. ...
Beijing Municipal Commission of Education disciplinary construction and Graduate Education construction projects. ...
doi:10.1109/aciids.2009.22
dblp:conf/aciids/NiuW09
fatcat:4lqksymmo5cgbeidppvhe7sqia
Multi-scale Convolutional Neural Network with Time-cognition for Multi-step Short-term Load Forecasting
2019
IEEE Access
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INDEX TERMS Short-term load forecasting, probabilistic load forecasting, multi-step, multi-scale convolution, time cognition, deep learning. ...
At first, a deep convolutional neural network model based on multi-scale convolutions (MS-CNN) extracts different level features that are fused into our network. ...
ACKNOWLEDGMENT The authors would like to thank Minghao Xie, Heng Guo, Minghao Wang, Suhan Cui and Chengwei Cai for their assistances on experiments. ...
doi:10.1109/access.2019.2926137
fatcat:ntmfurlqt5b55ftqzbyqqr3xze
Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model
2010
International Journal of Electrical Power & Energy Systems
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This paper proposes a new hybrid approach based on nonlinear chaotic dynamics and evolutionary strategy to forecast electricity loads and prices. ...
A comparison with other methods such as autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) is shown. ...
Acknowledgements The authors would like to thank the support of the Brazilian Institutions of CAPES (Project 023/05), CNPq and FAPEMIG. ...
doi:10.1016/j.ijepes.2009.06.018
fatcat:ihp2ks4bjzbbjjkuhmjv5cn73m
A hybrid SVR with the firefly algorithm enhanced by a logarithmic spiral for electric load forecasting
2022
Frontiers in Energy Research
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Accurate forecasting of an electric load is vital in the effective management of a power system, especially in flourishing regions. ...
Half-hourly electric load from five main regions (NSW, QLD, SA, TAS, and VIC) of Australia are used to train and test the proposed model. ...
Based on these, the prediction of short-term load in NSW, QLD, VIC, SA and TAS can be obtained. ...
doi:10.3389/fenrg.2022.977854
fatcat:k3zyhzgufngdlhzjtfl4zxpfva
A Performance Comparison of Neural Networks in Forecasting Stock Price Trend
2017
International Journal of Computational Intelligence Systems
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both in theory and practice. ...
As a form of artificial intelligence, neural network can fully reveal the complex relationship between investors and price fluctuations. ...
The forecasting method of chaos theory comprises local method, globe method, weighted local method, and so on. ...
doi:10.2991/ijcis.2017.10.1.23
fatcat:vcl6zoq2wzbupfbk62kau7zb7a
Using the Hierarchical Temporal Memory Spatial Pooler for Short-Term Forecasting of Electrical Load Time Series
2018
Applied Computing and Informatics
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In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). ...
The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. ...
In [8] , point short-term load forecasting was carried out based on Chaos theory and a radial basis function (RBF) neural network. ...
doi:10.1016/j.aci.2018.09.002
fatcat:uycntxmrercnfj2wa3fib3kebm
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