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Short-term load forecast of a low load factor power system for optimization of merit order dispatch using adaptive learning algorithm
2012
2012 International Conference on Power, Signals, Controls and Computation
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In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor ...
Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. ...
Modelling For studying the short term load forecasting of a low load factor system, Kerala power system was chosen. ...
doi:10.1109/epscicon.2012.6175280
fatcat:avlxoc5cirhdzkchq5i453ykqa
Corrective control through HVDC links: A case study on GB equivalent system
2013
2013 IEEE Power & Energy Society General Meeting
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Rapid change of active power through an LCC HVDC link could ensure transient stability of an AC system. This could be achieved by exploiting the short-term overload capability of the link. ...
scenarios and for a range of short-term overload capabilities. ...
HVDC power order), according to the short-term overload capability of the link. ...
doi:10.1109/pesmg.2013.6672890
fatcat:lljfs3u5tze5botyfhia7dhjwu
Voltage fluctuations in networks with distributed power sources
2012
2012 IEEE 15th International Conference on Harmonics and Quality of Power
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One of the electromagnetic disturbances generated by distributed power sources, e.g. wind turbines, are voltage fluctuations. ...
An imprecise prediction of the disturbance level may be the reason for erroneous decisions made at the stage of issuing technical conditions of connection. ...
The network was loaded with passive loads (of total power about 3000 kW and power factor 0.9) and a varying number of motors with different rated powers, loaded with torques of various magnitude and type ...
doi:10.1109/ichqp.2012.6381206
fatcat:rxp7nnwobbfnjj34fmdb7f4zba
Short-term Forecasting of the Abu Dhabi Electricity Load Using Multiple Weather Variables
2015
Energy Procedia
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Short term load forecasting, ranging from a few hours ahead to a few weeks ahead has great importance in the operations and planning of the electric power system. ...
With a more realistic scenario, where the exogenous variables are not known over the forecasting horizon and have to be forecasted before being used in the load forecast, the TF model had better accuracy ...
This paper focuses in modeling and forecasting short-term hourly load with a forecasting horizon of one day, two days or one week. ...
doi:10.1016/j.egypro.2015.07.616
fatcat:a3bu5jbjfzhirf5tfmleavkkty
Neuro-short-term load forecast of the power system in Kuwait
1997
Applied Mathematical Modelling
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This paper is concerned with short-term load forecast of the electrical power system in Kuwait. It applies artificial neural networks @NN's) to predict the half hour total system load. ...
This paper deals with short-term load forecasting and predicting the l/2-hr total system load with particular emphasis on the power system of Kuwait. ...
For short-term load forecasting it is essential to use all of the available predicted weather parameters to improve the accuracy of the forecasting model. ...
doi:10.1016/s0307-904x(96)00165-5
fatcat:kzvsp625ebfzdkro4uivpy6t7m
Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting
[chapter]
2017
Lecture Notes in Computer Science
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In this paper, we propose a boosting based framework for MKL regression to deal with the aforementioned issues for short-term load forecasting. ...
Computation time is an important issue for short-term load forecasting, especially for energy scheduling demand. However, conventional MKL methods usually lead to complicated optimization problems. ...
Simulation results on residential data show that the short-term electricity load forecasting could be improved with BMKR. ...
doi:10.1007/978-3-319-71273-4_4
fatcat:zg4rxinrozctbiicf4f3jfxxwe
Artificial Neural Network Approach For Short Term Load Forecasting For Illam Region
2007
Zenodo
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In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. ...
The short-term load forecasting (one to twenty four hours) is of importance in the daily operations of a power utility. ...
With the emergence of load management strategies, the short term load forecasting has played a greater role in utility operations. ...
doi:10.5281/zenodo.1328641
fatcat:3lc4fhpc2nbwdabxklgkg5xnnm
The Role of Learning Methods in the Dynamic Assessment of Power Components Loading Capability
2005
IEEE transactions on industrial electronics (1982. Print)
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The need for dynamic loading of power components in the deregulated electricity market demands reliable assessment models that should be able to predict the thermal behavior when the load exceeds the nameplate ...
This paper discusses an innovative grey-box architecture for integrating physical knowledge modeling (a.k.a. whitebox) with machine learning techniques (a.k.a. black-box). ...
As for the medium and long-term load capability estimation, they are oriented to establish an acceptable level of power transfer for a defined time period. ...
doi:10.1109/tie.2004.841072
fatcat:wji3w5l6hvfohbhqg47s23gpfi
A Hybrid Short-Term Power Load Forecasting Model Based on the Singular Spectrum Analysis and Autoregressive Model
2014
Advances in Electrical Engineering
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method has a better performance in terms of short-term power load forecasting. ...
Short-term power load forecasting is one of the most important issues in the economic and reliable operation of electricity power system. ...
of the generators are based on the short-term power load forecasting. ...
doi:10.1155/2014/424781
fatcat:vqvgr5uczvgy7ikesuxuhca7eq
Real-Time Short-Term Voltage Stability Assessment Using Combined Temporal Convolutional Neural Network and Long Short-Term Memory Neural Network
2022
Applied Sciences
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This research presents a new method based on a combined temporal convolutional neural network and long-short term memory neural network for the real-time assessment of short-term voltage stability to keep ...
The trained model uses the time series post-disturbance bus voltage trajectories as the input in order to predict the stability state of the power system in a computationally efficient manner. ...
Short-term voltage instability is caused by the dynamics of induction motor load, electronically controlled load, HVDC link power regulation, and inverter-interfaced renewable generators [2, 3] . ...
doi:10.3390/app12136333
fatcat:2xm7hiluc5fyhj7bgacefnewdq
Comparison of very short-term load forecasting techniques
1996
IEEE Transactions on Power Systems
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The preliminary study shows that it is feasible to design a simple, satisfactory dynamic forecaster to predict the very short-term load trends on-line. ...
Three practical techniques --Fuzzy Logic (F' L), Neural Networks (NN), and Auto-regressive model (AR) -for very short-term load forecasting have been propwed and discussed in this paper. ...
The authors would like to acknowledge the support provided by National Science Foundation under grant IRI-9216545 and by Electric Power Research Institute uinder grants RP8030-09 and RP3555-04. ...
doi:10.1109/59.496169
fatcat:qu6krpcpmjgwlltggkwdm4jw7m
A learning framework based on weighted knowledge transfer for holiday load forecasting
2018
Journal of Modern Power Systems and Clean Energy
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With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays. ...
We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities from Guangdong province to illustrate the ...
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution ...
doi:10.1007/s40565-018-0435-z
fatcat:hzlzwudqsverlawc3g7un346iq
Battery Monitoring and Power Management for Automotive Systems
2014
American Journal of Energy Research
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Also, Battery Monitoring and Power Management to provide cranking capability several days ahead is also analyzed. ...
Electrically controlled and powered systems for braking, steering and stabilization need a reliable supply of electrical energy. ...
An actual increase of battery impedance helps prediction of reduced short-term high power capability. ...
doi:10.12691/ajer-2-1-1
fatcat:dtawsrxuqbgjdclugpevrz3gae
A Comprehensive Study of Forecasting Problems and Methods in Power Systems
2016
International Journal of Engineering Research and
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Within paper likewise examines estimating problems connected including electricity price with load prediction. ...
Accessible estimating methods are audited including attention upon information taking out for expectation of wind power. ...
The forecasting of long-term wind power depends upon long-term designs of wind, while short-term with medium predictions are by and large for a couple of days (relies upon the business sector exercise, ...
doi:10.17577/ijertv5is020365
fatcat:tuukigjdrrdr7ef6m4p2wzow3u
A Novel Combined Approach for Daily Electric Load Forecasting Based on Artificial Neural Network and Modified Bat Algorithm
2017
International Journal of Computer Applications Technology and Research
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model variables for the purpose of electric daily load prediction. ...
In this paper a novel combined method based on Modified Bat Algorithm (MBA) and Neural Network algorithm has proposed in order to forecast the electric peak load power. ...
Electrical load forecasting is defined as an intelligent process that predict required electrical power for short-term, mediumterm, and long-term demand [7] . ...
doi:10.7753/ijcatr0612.1001
fatcat:kajrozdzujgufptirz643wkptq
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