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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

K. Pramelakumari, S. R. Anand, V. P. Jagathy Raj, E. A. Jasmin
2012 2012 International Conference on Power, Signals, Controls and Computation  
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

Inmaculada Martinez Sanz, Balarko Chaudhuri, Goran Strbac
2013 2013 IEEE Power & Energy Society General Meeting  
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

Maciej Mroz, Krzysztof Chmielowiec, Zbigniew Hanzelka
2012 2012 IEEE 15th International Conference on Harmonics and Quality of Power  
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

Luiz Friedrich, Afshin Afshari
2015 Energy Procedia  
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

Abdullah S. Al-Fuhaid, Mohamed A. El-Sayed, Magdi S. Mahmoud
1997 Applied Mathematical Modelling  
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]

Di Wu, Boyu Wang, Doina Precup, Benoit Boulet
2017 Lecture Notes in Computer Science  
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

Mohsen Hayati, Yazdan Shirvany
2007 Zenodo  
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

D. Villacci, G. Bontempi, A. Vaccaro, M. Birattari
2005 IEEE transactions on industrial electronics (1982. Print)  
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

Hongze Li, Liuyang Cui, Sen Guo
2014 Advances in Electrical Engineering  
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

Ananta Adhikari, Sumate Naetiladdanon, Anawach Sangswang
2022 Applied Sciences  
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

K. Liu, S. Subbarayan, R.R. Shoults, M.T. Manry, C. Kwan, F.I. Lewis, J. Naccarino
1996 IEEE Transactions on Power Systems  
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

Pan ZENG, Chang SHENG, Min JIN
2018 Journal of Modern Power Systems and Clean Energy  
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://, which permits unrestricted use, distribution  ... 
doi:10.1007/s40565-018-0435-z fatcat:hzlzwudqsverlawc3g7un346iq

Battery Monitoring and Power Management for Automotive Systems

Anila Thyagarajan, R. Raja Prabu, G. Uma
2014 American Journal of Energy Research  
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

Kumar Gaurav Singh, Kishan Bhushan Sahay
2016 International Journal of Engineering Research and  
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

Eduardo Capra, Hugo Ribeiro
2017 International Journal of Computer Applications Technology and Research  
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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