Short Term Load Forecasting Using Ann Thesis
In thisdissertation a short-term load forecasting model is introduced(Rule-Based ANN model).
Short term load forecasting using ann thesis
The electric Power industry is currently undergoing an unprecedented reform. One of the most exciting and potential benefit of recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview as well as the techniques for the Short term load forecasting using the weather parameter like rainfall and implementing a neural network techniques in the power systems. This prediction shows a combined approach of predicted rainfall and the ANN will help for the better forecasting of electric load at city scale.
Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This dissertation focuses on study of short term load forecasting using different types of computational intelligence methods. It
uses evolutionary algorithms (i.e. Genetic Algorithm, Particle Swarm Optimization, Artificial Immune System), neural networks (i.e. MLPNN, RBFNN, FLANN, ADALIN, MFLNN, WNN, Recurrent NN, Wilcoxon NN), and fuzzy systems (i.e. ANFIS). The developed methods give load forecasts of one hour upto 24 hours in advance. The algorithms and networks were have been demonstrated using simulation studies. The power sector in Orissa has undergone various structural and organizational changes in recent past. The main focus of all the changes initiated is to make the power system more efficient, economically viable and better service oriented. All these can happen if, among other vital factors, there is a good and accurate system in place for forecasting the load that would be in demand by electricity customers. Such forecasts will be highly useful in proper system planning & operations. The techniques proposed in this thesis have been simulated using data obtained from State Load Dispatch Centre, Orissa for the duration September – 2006 to August – 2007.
Short-term forecasting using ANN ..
—Short Term Load Forecasting (STLF) is basically aimed at predicting load with a leading time of one hour to seven days. This paper proposes the application of AI techniques such as Artificial Neural Network (ANN) and Wavelet Neural Network (WNN) for Short Term Load Forecasting (STLF). An accurate forecast eases the problem of generation and load management to a great extent. The STLF is determined for a day ahead using ANN and WNN methods. The comparison and the performance of the two methods are summarized and further the normalized MAPE error for a day ahead is depicted in the paper. The Validation is also carried out for the TNEB test system.
Short Term Load Forecasting in Interconnected Greek Power ... The targets of this paper are: (1) ... the ANN short-term load forecasting method of the next day in ... T h e R e s e a r c h B u l l e t i n o f J o r d a n A C M ... Forecasting Electrical Load using ANN Combined ... This paper combined artificial neural network and ... instrument for short, medium and long term load forecasting ... Effect of temperature on short term load forecasting using an ... ... (ANN) approach to Short-Term Load Forecasting ... approach to short term load forecasting, M. Tech. Thesis, ... artificial neural network short term load ... Short-Term Load Forecasting using PSO Based Local Linear ... Short-Term Load Forecasting using PSO Based ... The work presented in this paper makes use of PSO ... part of the input of short term load forecasting(STLF) Short Term Load Forecasting OF 132/33Kv Maiduguri ... Short Term Load Forecasting OF 132/33Kv Maiduguri Transmission Substation Using Artificial Neural Network (ANN) Short-term load forecasting of UPPCL using ANN ... Short-term load forecasting of UPPCL using ... paper discusses role of ANN in day-ahead hourly forecast of the power system load in UPPCL so as to minimize the ... Short Term Load Forecasting Using Neural Network Trained with ... Short Term Load Forecasting using Neural Network trained with Genetic ... The paper has demonstrated the use of GA & PSO ... ANN short term load forecasting model. Application of Artificial Neural Networks to Short Term Load ... Application of Artificial Neural Networks to Short Term Load Forecasting. ... Application of Artificial Neural Networks to Short Term Load Forecasting. PhD thesis, ... Short Term Load Forecasting for Erbil Distribution System ... ... and Artificial Neural Network (ANN). ... Short Term Load Forecasting (STLF) ... it is not so effective in capturing short duration A Simple Hybrid Model for Short-Term Load Forecasting The paper proposes a simple ... "Short-term load forecasting using threshold ... "Hybrid adaptive techniques for electric-load forecast using ANN and ...
IJCTT - Short Term Load Forecasting Using ANN and …
Load forecasting is the prediction of future loads of a power system. It is an important component for power system energy management. Precise load forecasting helps to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. By forecasting, experts can have an idea of the loads in the future and accordingly can make vital decisions for the system. This work presents a study of short-term hourly load forecasting using different types of Artificial Neural Networks.
A new hybrid technique using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to forecast the next 24 hours load is proposed in this paper. The forecasted load for the next 24 hours is obtained by using four modules consisting of the Basic SVM, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner. Thesemodules try to extract the various components like Basic component, Peak and Valley components, Average component, Periodic component and random component of a typical weekly load profile. The Basic SVM uses the historical data of load and temperature to predict the next 24 hours load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures respectively. The Averager captures the average variation of the load from the previous load behaviour, while the Adaptive Combiner uses the weighted combination of outputs from the Basic SVM and the Forecaster, to forecast the final load. The statistical and artificial intelligence based methods are conceptually incorporated into the architecture to exploit the advantages and disadvantages of each technique.
Short Term Load Forecasting Using ANN and WNN
IJCTT - Short Term Load Forecasting Using ANN and WNN
Kumar, Manoj (2009) Short-Term Load Forecasting using Artificial Neural Network Techniques. BTech thesis.
Short Term Load Forecasting (STLF) Using Artificial …
) Ph. Department of Counseling short term load forecasting using ann thesis and Special Populations. E
Short, Medium and Long Term Load Forecasting Model …
An integrated Artificial Neural Network (ANN) approach to Short-Term Load Forecasting (STLF) is proposed in this paper
Short-Term City Electric Load Forecasting with …
2.3.4 Evolution of Artificial Neural Network Master Thesis Neural Network – …9.7/10 · Comparison of Performance Analysis using Di erent Neural · PDF fileComparison of Performance Analysis using 2013 Professor, CSE Department NIT Rourkela, In this thesis simple feed forward neural network Short-term Electrical Load Forecasting for an · PDF file8-2013 Short-term Electrical Load Forecasting for an Institutional/Industrial Power System Using an Artificial Neural Network A Thesis Presented for the Master of Neural Network Based Microgrid Voltage Control · PDF fileMay 2013 Neural Network Based Microgrid Voltage Control this thesis examines the neural network algorithm that can be There are many different types of neural .Road Safety Assessment of U.S.
Short-term load forecasting using a Gaussian process …
Salman Quaiyum, Yousuf Ibrahim Khan, Saidur Rahman and Parijat Barman. Article: Artificial Neural Network based Short Term Load Forecasting of Power System. 30(4):1-7, September 2011. Full text available.
Short-term load forecasting using a Gaussian process ..
Kerim Allahverdiev, Azerbaijanian by birth, was born in 1944 and educated at the Moscow Power Engineering Institute (MEI), where he received degree in Electrical Engineering in 1967. His Institute diploma thesis was performed at the Lebedev Institute of Physics, Moscow and was devoted to the superconducting properties of layered Niobium Selenide crystals. In 1967 he finished 2 years English school in Moscow. In 1972 he received the degree of the Candidate of Physical Mathematical Sciences working at the Institute of Physics Azerbaijan National Academy of Sciences in close collaboration with the Lebedev Institute of Physics. In 1974-1975 he had Postdoctoral at the Clarendon Laboratory of Oxford University, UK. In 1982 he received a degree of Doctor of the Physical Mathematical Sciences submitting the thesis to the Institute of General Physics also, Moscow, working in close collaboration with the Institute of Spectroscopy and Institute of High Pressure Physics, Troitsk, Moscow Region. Since 1985 he is Professor in Physics. In 1992-1995 he is Professor in Physics at the Middle East Technical University, Ankara, Turkey. Since 1995 he is Senior Scientific Researcher at the Marmara Research Centre (MRC) of the Turkish Scientific and Technological Council (TUBITAK), Gebze, Turkey and Senior Research Scientist at the Institute of Physics Azerabaijan National Academy of Sciences.
As a visiting professor, researcher and invited lecturer, Prof. K. Allahverdiev has presented, taught seminars and engaged in scientific collaboration at more than 40 Universities and Research Centers around the world, including Moscow State University; Oxford University, Cambridge University; Sheffield University, UK; London University; Imperial College, UK; MPI FKF, Stuttgart, Germany; RWTH Aachen, Germany; Bochum University, Germany; Bayreuth University, Germany; Hamburg University, Germany; US Air Force Wright Patterson Lab., Dayton; Colorado State University, USA; University of Cincinatti, USA; Tsukuba University, Japan and Madrid University, Spain.
He has been directing academic research in the field of physics and practical applications of layered semiconductors for over 30 years. Research Achievements include: new effective nonlinear materials in the system of layered gallium selenide- type semiconductors; first observation and explanation the nature of the low-temperature ferroelectric and high-pressure phase transitions in ternary layered chalcogenides. New class of the ferroelectric-semiconductors was discovered in a frame of joint research with the Institute of Spectroscopy (Prof. E. Vinogradov et al.), Troitsk, Moscow Region; first experimental investigation of the influence of ultra-short laser pulses on the transient-transmission change of layered A3B6 crystals and observation of quantum beats as due to the coherently excited fully symmetric phonons. As a result, new type of ultra-fast light modulator was suggested; first observation of the second harmonic generation in gallium selenide at 10.6 µm an 1579 nm and resonant excitonic second-harmonic generation; influence of intercalation on the electronic and vibration properties of gallium selenide-type crystals.
K. Allahverdiev hands-on experience in: modern spectroscopy techniques-also under pressure (pump-probe experiments, Raman scattering, nonlinear harmonic generation and wave mixing, photo- and electro- luminescence, exciton spectroscopy and others; growth and characterization of single crystals, nanocrystals and polycrystalline materials; carrier transport and galvanomagnetic measurements, dielectric spectroscopy; supervising the students at graduate and undergraduate levels, advising Ph.D Theses; demonstrated ability in project management, communication and organization skill, energetic.
Professor Allahverdiev has received several awards, honors, membership and fellowships including Azerbaijan State Prize in Science (1988); Krupp's stipendium, Technical University Aachen (1989); Window-on- Science Award, US Air Force European Office of Aerospace R&D, USA (1996, 2001); Royal Society Award as visiting Professor (1987, 1989); Citation in the USSR Academy of Sciences List of Best Achievements of the Year for the determination of the interlayer parameters and the peculiarities of the phonon spectra of A3B6 semiconductors (1978). Same Citation for different achievements in 1983, 1989 and 1991. He is a member: of New York Academy of Sciences (1998); Azerbaijan National Academy of Creation (1988); Russian Engineering Academy of Sciences, named by A. M. Prokhorov (2008); Member of the Organizing Committee of the European High Pressure Research Group (EHPRG) (1987-1990, 1991-1994, 1996-1999); Member of the Editorial Board, Turkish Journal of Physics; Reviewer of the JOSA, JAP, Materials Research Bulletin and others.
Professor Allahverdiev has published more than 275 articles on the linear and nonlinear optical properties of layered semiconductors, 1 book and 7 review articles. He has 5 patents.
Although a very busy personality Professor Allahverdiev finds time for sport (football, swimming). Among his other hobbies are gardening, walking, music (classic and modern).
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