2014
3
10
10
0
Implementation of VlSI Based Image Compression Approach on Reconfigurable Computing System  A Survey
2
2
Image data require huge amounts of disk space and large bandwidths for transmission. Hence, imagecompression is necessary to reduce the amount of data required to represent a digital image. Thereforean efficient technique for image compression is highly pushed to demand. Although, lots of compressiontechniques are available, but the technique which is faster, memory efficient and simple, surely hits theuser requirements. In this paper, the image compression, need of compression, its principles, how imagedata can be compressed, and the image compression techniques are reviewed and discussed. Also,waveletbased image compression algorithm using Discrete Wavelet Transform (DWT) based on Bsplinefactorization technique is discussed in detail. Based on the review, some general ideas to choose the bestcompression algorithm for an image are recommended. Finally, applications and future scopes of imagecompression techniques are discussed considering its development on FPGA systems.
1

1
7


Shahin
Shafei
Iran
shahin_shafei@yahoo.com
Image compression
Discrete wavelet transform
Decomposed lifting algorithm (DLA)
Huffmancoding
[[1] ChaoTsung Huang, PoChih Tseng And##LiangGee Chen, "VLSI Architecture for##Forward Discrete Wavelet Transform Based##on Bspline Factorization", Journal of VLSI##Signal Processing, 40,pp. 343353, 2005.##[2] ChaoTsung Huang, PoChih Tseng, and##LiangGee Chen, "Analysis and VLSI##Architecture for 1D and 2D Discrete##Wavelet Transform", IEEE Transactions On##Signal Processing, Vol. 53, No. 4, APRIL##[3] Xixin Cao, Qingqing Xie, Chungan Peng,##Qingchun Wang, Dunshan Yu, "An Efficient##VLSI Implementation of Distributed##Architecture for DWT", Multimedia Signal##Processing, 2006 IEEE 8th Workshop on, pp.##364  367, Oct. 2006.##[4] Kai Liu, KeYan Wang, YunSong Li and##ChengKe Wu, "A novel VLSI architecture##for realtime linebased wavelet transform##using lifting scheme", Journal of Computer##Science and Technology, Vol. 22, no. 5,##September 2007##[5] Wang Chao and Cao Peng, "Efficient##Architecture for 2Dimensional Discrete##Wavelet Transform with Novel Lifting##Algorithm", Chinese Journal of Electronics,##Vol.19, No.1, Jan. 2010.##[6] Mohsen Amiri Farahani, Mohammad Eshghi,##"Implementing a New Architecture of##Wavelet Packet Transform on FPGA",##Proceedings of the 8th WSEAS International##Conference on Acoustics & Music: Theory &##Applications, Vancouver, Canada, pp.19##21,June 2007.##[7] Maria A. Trenas, Juan Lopez, Emilio L.##Zapata, “FPGA Implementation of Wavelet##Packet transform with Reconfigurable Tree##Structure”, Euro micro Conference, 2000.##Proceedings of the 26th Volume 1, pp. 244 ##251,57Sept. 2000##[8] Kumar Gupta, A.; Dyer, M.; Hirsch, A.;##Nooshabadi, S.; Taubman, D.; "Design of a##single chip block coder for the EBCOT##engine in JPEG2000", Proceedings of the##48th Midwest Symposium on Circuits and##Systems, pp: 63  66, 2005.##[9] C. Hemasundara Rao and M. Madhavi Latha,##"A Novel VLSI Architecture of Hybrid##Image Compression Model based on##Reversible Blockade Transform", World##Academy of Science, Engineering and##Technology 52 2009.##[10] Isa Servan Uzun, Abbes Amira, "Realtime##2D wavelet transform implementation for##HDTV compression", RealTime Imaging 11##pp.151165,2005.##[11] Jagadish H. Pujar, Lohit M. Kadlaskar, “A##New Lossless Method Of Image##Compression And Decompression Using##Huffman Coding Techniques”, Journal of##Theoretical and Applied Information##Technology, Vol. 15, No.1, 2010.##]
Reinforcement Learning Based PID Control of Wind Energy Conversion Systems
2
2
In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability and safe performance. Thus, areinforcement learning algorithm is used for online tuning of PID coefficients in order to enhance closed loopsystem performance. In this study, at start the proposed controller is applied to two pure mathematical plants,and then the closed loop WECS behavior is discussed in the presence of a major disturbance.
1

8
15


Mohammad Esmaeil
akbari
Iran
makbari@iauahar.ac.ir


Noradin
Ghadimi
Iran
noradin.1364@gmail.com
Adaptive Control
WECS
Reinforcement learning
[[1] K. Ogawa, N.Ymammura, M.Ishda, Study for Small##Size Wind Power Generating System Using##Switched Reluctance Generator, IEEE International##Conference on Industrial Technology, 2006, pp.##15101515,##[2] S. Manesis, Fuzzy Logic Control Development in##SCADA Software Frameworks, International##Review of Automatic Control,##[3] F. D. Bianchi, H. De Battista and R. J. Mantz,##“Wind Turbine Control Systems Principles,##Modeling and Gain Scheduling Design” Springer##Verlag London Limited 2007.##[4] Miguel Angel Mayosky, Gustavo I.E.Cancelo##“Direct Adaptive Control of Wind Energy##Conversion Systems Using Gaussian Networks”##IEEE Trans on Neural Networks,Vol.10, pp.898##906, July 1999##[5] M.Sedighizade, A.Rezazadeh “Adaptive PID##Control of Wind Energy Conversion Systems Using##RASP1 Mother Wavelet Basis Function Networks”##Proceedings of Academy of Science, Engineering##and Technology Vol.27, pp.269273. February 2008##[6] M.Sedighizade “Nonlinear Model Identification and##Control of Wind Turbine Using Wavelets"##Proceedings of the 2005 IEEE Conference on##Control Applications Toronto, pp.10571062##Canada, 2005##[7] M. Kalantari, M. Sedighizadeh “Adaptive Self##Tuning Control of Wind Energy Conversion##Systems Using Morlet Mother Wavelet Basis##Functions Networks”12th Mediterranean IEEE##Conference on Control and Automation MED’04,##Kusadasi, Turkey, 2004.##[8] X. Zhang, D. XU and Y. LIU, “Predictive##Functional Control of a Doubly Fed Induction##Generator for Variable Speed Wind Turbines,” 5th##World Congress on Intelligent Control and##Automation, June 15 19, Hangzhou. P.R. China,##[9] Damien Ernst,et al ”Power System Stability Control:##Reinforcement Learning Framework”IEEE##Transaction on Power System,Vol.19,No.1,February##[10] R. Bellman, ”Dynamic Programming”.##Princeton, NJ: Princeton Univ. Press, 1957.##[11] P.Puleston”Control strategies for wind energy##conversion systems”Ph.D.dissertation,Univ.La Plata.##Argentina 1997##]
Training Set of Data Bin for Small Black Pixels Neighborhood Recognition of Each Boundary
2
2
We first describe how to “fuzzify” the estimated binary columns to create a [0,1]valued column. Werefer to this [0,1] valued column as the soft segmentation column of the noisy spectrogram column.Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply asthe soft segmentation. The banddependent posterior probability that the hard segmentation columnvalue of pixel is 1, given that bin and the binary values in the neighborhood configuration of the pixelare equal. Symbolically, each pixel of the soft segmentation column is set to the soft segmentationcolumn value of the pixel in a row was set to zero.
1

16
19


Roya
Abdollahi
Iran
roya.abdollahi6666@yahoo.com
[[1] Sedghi T., “A Fast and Effective Model for##cyclic Analysis and its application in##classification” Arabian Journal for Science##and Engineering Vol. 38 October 2012.##[2] Sedghi T., Amirani C. M., Fakheri M.,”##Robust and Effective Framework for Image##Retrieval Scheme using Shift Invariant##Texture and Shape Features” International##Journal of Natural and Engineering Sciences##4 (1): 95101, Vol. 3, Dec, 2010.##]
Fuel Cell Voltage Control for Load Variations Using Neural Networks
2
2
In the near future the use of distributed generation systems will play a big role in the production ofelectrical energy. One of the most common types of DG technologies , fuel cells , which can be connectedto the national grid by power electronic converters or work alone Studies the dynamic behavior andstability of the power grid is of crucial importance. These studies need to know the exact model of dynamicelements. In this paper, a new method based on a neural network algorithm for controlling the fuel cellvoltage is provided. The effects of load change the output voltage characteristic of the fuel cell unit ischecked Simulations in MATLAB / SIMULINK. The results show that the prosecution is conducted in anappropriate manner Voltage Stabilization time.
1

20
23


Zolekh
Teadadi
Iran
sh.teadadi@gmail.com


Hassan
Changiziyan
Iran
Fuel cell
Dynamic behavior
Neural networks
hydrogen
neural network controller
[[1]M.R. Ashraf Khorasani, M.Zamani, S.##Asghari, B.fageh emani, Winter 1388,##"Design 5 kW fuel cell system," Journal of##[2]M. Samavati, et al, 1388," report on the##conceptual design of the project design and##fuel cell CHP systems 5 KW", fuel cell##groups, Research Center of Engineering##[3]N.Frkhzadarshad,S.A.Purmusavy,."Simulatio##n and modeling of dynamic fuel cell systems##for distributed generation applications", the##sixth conference on energy, FPRE27298##[4] C. Tabatabai, 1385 "fuel cell technology and##its application in the car", Futures##Conference, technology eyes expansion##[5]H. Dilafruz, 1385, "Estimation of sediment##Intelligent Artificial Neural Networks",##Thesis, Faculty of Engineering, University of##[6]J.Arkart,M. Ghazanfari,1383, "Neural##Networks (functional principle)" University##of Science and Technology Iran##[7] V.R, V.Paul,"Neural Network Based Control##for PEM Fuel Cells"IOSR Journal of##Electronics & Communication Engineering##(IOSRJECE) ISSN (e): 22781684 ISSN##(P):2320334x, pp4752##[8]M.Hatti,M.Tioursi,"Dynamic Neural Network##Controller Model of PEM Fuel Cell System",##International Journal of Hydrogen Energy,##34(2009)50155021##]
Load Frequency Control in Two Area Power System Using Sliding Mode Control
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2
In this article, the sliding mode control of frequency load control of power systems is studied. The study areaconsists of a system of water and heat. First, a mathematical model of the proposed system disturbances ismade and then sliding control mode for frequency load control is provided. By the system simulation andsliding mode control, it can be shown that the damping of oscillations is well led.
1

24
36


Milad
Babakhani Qazijahan
Iran
babakhani.milad@yahoo.com
frequency Load control
sliding mode control
dual zone system
[[1] AlMusabi, Naji A. "Design of optimal##variable structure controllers: applications##to power system dynamics". Diss. Master’s##thesis, King Fahd University of Petroleum##and Minerals, Dhahran, Saudi Arabia, 2004.##[2] H. Bevrani, T. Hiyama, Robust##decentralised PI based LFC design for time##delaypower systems, Energy Conversion##and Management 49 (2) (2008) 193–204.##[3] Y. Rebours, D. Kirschen, M. Trotignon, S.##Rossignol, A survey of frequency##andvoltage control ancillary services—Part##I: Technical features, IEEETransactionson##Power Systems 22 (1) (2007) 350–357.##[4] J. Frunt, A. Jokic, W. Kling, J. Myrzik, P.##van den Bosch, Provision of##ancillaryservices for balance management##in autonomous networks, in: Proceedings##ofthe 5th International Conference on##European Electricity Market, EEM##2008,2008, pp. 1–6.##[5] H. Shayeghi, H. Shayanfar, A. Jalili, Load##frequency control strategies: a stateoftheart##survey for the researcher, Energy##Conversion and Management 50(2) (2008)##344–353.##[6] E. Cam, Application of fuzzy logic for load##frequency control of hydroelectricalpower##plants, Energy Conversion and##Management 48 (4) (2007) 1281–1288.##[7] H. Shayeghi, H. Shayanfar, Application of##ANN technique based on _synthesisto load##frequency control of interconnected power##system, International Journalof Electrical##Power & Energy Systems 28 (7) (2006)##503–511.##[8] Venkat, Aswin N., et al. "Distributed output##feedback MPC for power system##control." Decision and Control, 2006 45th##IEEE Conference on. IEEE, 2006.##[9] S. Velusami, I. Chidambaram,##Decentralized biased dual mode controllers##forload frequency control of interconnected##power systems considering GDBand GRC##nonlinearities, Energy Conversion and##Management 48 (5) (2007)1691–1702.##[10] S. Hosseini, A. Etemadi, Adaptive term##neurofuzzy inference system##basedautomatic generation control, Electric##Power Systems Research 78 (7)##(2008)1230–1239.##[11] M. Alrifai, Decentralized controllers for##power system load frequency##control,ICGST International Journal on##Journal of Artificial Intelligence in Electrical Engineering, Vol. 3, No. 10, September 2014##Automatic Control and System Engineering##5(2) (2005) 1–16.##[12] H. Shayeghi, H. Shayanfar, A. Jalili, Load##frequency control strategies: a stateoftheart##survey for the researcher, Energy##Conversion and Management 50(2) (2008)##344–353.##[13] Vrdoljak, Krešimir, Nedjeljko Perić, and##Ivan Petrović. "Sliding mode based loadfrequency##control in power##systems." Electric Power Systems##Research80.5 (2010): 514527.##[14] V. Utkin, Sliding Modes in Control##Optimisation, SpringerVerlag, 1992.##[15] K. Vrdoljak, V. Tezak, N. Peric, A sliding##surface design for robust load frequency##control in power systems, in: Proceedings##of the Power Tech 2007, Lausanne,##Switzerland, 2007, pp. 279–284,##http://ewh.ieee.org/conf/ powertech.##[16] S. Spurgeon, Hyperplane design techniques##for discretetime variable structure control##systems, International Journal on Control##55 (2) (1992) 445–456.##[17] T. Radosevic, K. Vrdoljak, N. Peric,##Optimal sliding mode controller forpower##system’s loadfrequency control, in:##Proceedings of the 43rd##InternationalUniversities Power##Engineering Conference, Padova, Italy,##2008,pp. 1–5.##[18] K. Vrdoljak, N. Peric, M. Mehmedovic,##Optimal parameters for sliding modebased##loadfrequency control in power systems,##in: Proceedings of the 10thInternational##Workshop on Variable Structure Systems,##Antalya, Turkey, 2008,pp. 331–336##]
Neural Networks in Electric Load Forecasting:A Comprehensive Survey
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2
Review and classification of electric load forecasting (LF) techniques based on artificial neuralnetworks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANNoriented applications for forecasting are given in the literature. These are classified into five groups:(1) ANNs in shortterm LF, (2) ANNs in midterm LF, (3) ANNs in longterm LF, (4) Hybrid ANNs inLF, (5) ANNs in Special applications of LF. The major research articles for each category are brieflydescribed and the related literature reviewed. Conclusions are made on future research directions.
1

37
50


Vahid
Mansouri
Iran
vahidmansouri2010@gmail.com


Mohammad Esmaeil
akbari
Iran
Artificial Neural Networks (ANNs)
Load Forecasting(LF)
Short Term LF
Mid Term LF
Long Term LF
Peak LF
Unit Commitment(UC)
[[1] SRINIVASAN, D., and LEE, M. A.,##1995, Survey of hybrid fuzzy neural##approaches to electric load forecasting.##Proceedings of the IEEE International##Conference on Systems, Man and##Cybernetics, Part 5, Vancouver, BC, pp.##40044008.##[2] K. L. Ho, Y. Y. Hsu, C. F. Chen, T. E.##Lee, C. C. Liang, T. S. Lai, and K. K.##Chen, “Short term load forecasting of##Taiwan power system using a knowledgebased##expert system,” IEEE Trans.##Power Systems, vol. 5, no. 4, pp. 1214–##1221, 1990.##[3] S. Rahman and O. Hazim, “A generalized##knowledgebased shortterm loadforecasting##technique,” IEEE T. Power##Syst, vol. 8, no. 2, pp. 508–514, 1993.##[4] RustumMamlook , Omar Badran,##EmadAbdulhadi, A fuzzy inference##model for shortterm load forecasting,##Energy Policy Volume 37 issue 4, 2009.##[5] HUANG Jing, MA Jing, XIAO Xian##Yong, MidLong Term Load Forecasting##Based on Fuzzy Optimal Theory, IEEE##AsiaPacific Power and Energy##Engineering Conference (APPEEC),##[6] Juan J. Cárdenas, Luis Romeral, Antonio##Garcia, Fabio Andrade, Load forecasting##framework of electricity consumptions##for an Intelligent Energy Management##System in the userside, Expert Systems##with Applications Volume 39 issue 5,##[7] I. Moghram and S. Rahman, “Analysis##and evaluation of five shortterm load##forecasting techniques,” IEEE Trans.##Power Systems, vol. 4, no. 4, pp. 1484–##1491, 1989.##[8] YoungMin Wi, SungKwan Joo, and##KyungBin Song, Holiday Load##Forecasting Using Fuzzy Polynomial##Regression With Weather Feature##Selection and Adjustment, IEEE##Transactions on Power Systems Volume##27 issue 2, 2012. ##[9] A. G. Bakirtzis, J. B. Theocharis, S. J.##Kiartzis, and K. J. Satsios, “Shortterm##load forecasting using fuzzy neural##networks,” IEEE Trans. Power Systems,##vol. 10, no. 3, pp. 1518–1524, 1995.##[10] S.E. Papadakis, J.B. Theocharis, A.G.##Bakirtzis, load curve based fuzzy##modeling technique for shortterm load##forecasting, Fuzzy Sets and Systems##Volume 135 issue 2, 2003.##[11] S. E. Papadakis, J. B. Theocharis, S. J.##Kiartzis, and A. G. Bakirtzis, “A novel##approach to shortterm load forecasting##using fuzzy neural networks,” IEEE##Trans. Power Systems, vol. 13, no. 2, pp.##480–492, 1998.##[12] H. Mori and H. Kobayashi, “Optimal##fuzzy inference for shortterm load##forecasting,” IEEE Trans. Power##Systems, vol. 11, no. 1, pp. 390–396,##[13] T. Czernichow, A. Piras, K. Imhof, P.##Caire, Y. Jaccard, B. Dorizzi, and A.##Germond, “Short term electrical load##forecasting with artificial neural##networks,” Engineering Intelligent Syst.,##vol. 2, pp. 85–99, 1996.##[14] A. Khotanzad, R. AfkhamiRohani, and##D. Maratukulam , “ANNSTLF—##Artificial neural network shortterm load##forecaster— Generation three,” IEEE##Trans. Power Systems, vol. 13, no. 4, pp.##1413–1422, 1998.##[15] H. Hippert, C. Pedreira, and R. Souza,##“Neural networks for shortterm load##forecasting: A review and evaluation,”##IEEE Trans. Power Syst., vol. 16, no. 1,##pp. 44–55, Feb. 2001.##[16] M. Kazeminejad, M. Dehghan, M. B.##Motamadinejad, H. Rastegar, New Short##Term Load Forecasting Using Multilayer##Perceptron, IEEE International##Conference on Information and##Automation  Colombo, Sri Lanka ,##2006.12.1 .##[17] Ayca Kumluca Topalli ,Ismet Erkmen,##Ihsan To palli Intelligent , shortterm load##forecasting in Turkey, Electrical Power##and Energy Systems 28 (2006) 437–447.##[18] Philippe Lauret , Eric Fock, Rija N.##Randrianarivony, JeanFrancois##ManicomRamsamy, Bayesian neural##network approach to short time load##forecasting, Energy Conversion and##Management 49 (2008) 1156–1166.##[19] Zhi Xiao, ShiJie Ye, Bo Zhong, CaiXin##Sun, BP neural network with rough set##for short term load forecasting, Expert##Systems with Applications 36 (2009)##273–279.##[20] Abbas Khosravi, Saeid Nahavandi and##Doug Creighton, Construction of Optimal##Prediction Intervals for Load Forecasting##Problems, IEEE TRANSACTIONS ON##POWER SYSTEMS, VOL. 25, NO. 3,##AUGUST 2010.##[21] Ali Deihimi, Hemen Showkati,##Application of echo state networks in##shortterm electric load forecasting,##Energy 39 (2012) 327e340.##[22] Yongli Wang, Dongxiao Niu, Li Ji, Shortterm##power load forecasting based on##IVLBP neural network technology, The##2nd International Conference on##Complexity Science & Information##Engineering, Systems Engineering##Procedia 4 (2012) 168 – 174.##[23] M. Lópeza, S. Valeroa, C. Senabrea, J.##Apariciob, A. Gabaldonc, Application of##SOM neural networks to shortterm load ##forecasting: The Spanish electricity##market case study Electric Power Systems##Research 91 (2012) 18– 27.##[24] Adiga S. Chandrashekaraa, T.##Ananthapadmanabhab, A.D. Kulkarnib, A##neuroexpert system for planning and##load forecasting of distribution systems,##Electrical Power and Energy Systems 21##(1999) 309–314.##[25] M. Ghiassi, David K. Zimbra, H. Saidane,##Medium term system load forecasting##with a dynamic artificial neural network##model, Electric Power Systems Research##76 (2006) 302–316.##[26] Pituk Bunnoona, Kusumal##Chalermyanonta, Chusak Limsakula ,##MidTerm Load Forecasting: Level##Suitably of Wavelet and Neural Network##based on Factor Selection , International##Conference on Advances in Energy##Engineering, Energy procedia 14(2012),##[27] Arash Ghanbari, S. FaridGhaderi, M. Ali##Azadeh, Adaptive NeuroFuzzy Inference##System vs. Regression Based Approaches##for Annual Electricity Load Forecasting,##IEEE 2nd International Conference on##Computer and Automation Engineering##(ICCAE 2010) – Singapore.##[28] Shahid M. Awan, Zubair. A. Khan, M.##Aslam, Waqar Mahmood, Affan Ahsan,##Application of NARX based FFNN, SVR##and ANN Fitting models for long term##industrial load forecasting and their##comparison, IEEE 21st International##Symposium on Industrial Electronics##(ISIE)  Hangzhou, China, 2012.##[29] KwangHo Kim, HyoungSun Youn and##YongCheol Kang, ShortTerm Load##Forecasting for Special Days in##Anomalous Load Conditions Using##Neural Networks and Fuzzy Inference##Method, IEEE TRANSACTIONS ON##POWER SYSTEMS, VOL. 15, NO. 2,##[30] V.S. Kodogiannis, E.M. Anagnostakis,##Soft computing based techniques for##shortterm load forecasting, Fuzzy Sets##and Systems 128 (2002) 413–426.##[31] NimaAmjady, Farshid Keynia, Midterm##load forecasting of power systems by a##new prediction method, Energy##Conversion and Management, 49 (2008)##2678–2687.##[32] Titti Saksornchai, WeiJen Lee, Kittipong##Methaprayoon, James R. Liao and##Richard J. Ross, Improve the Unit##Commitment Scheduling by Using the##NeuralNetworkBased ShortTerm Load##Forecasting, IEEE TRANSACTIONS##ON INDUSTRY APPLICATIONS, VOL.##41, NO. 1, JANUARY/FEBRUARY##[33] A. J.Wood and B. F.Wollenberg, Power##Generation Operation and Control. New##York: Wiley, 1996.##[34] G. B. Sheblé and G. N. Fahd, “Unit##commitment literature synopsis,” IEEE##Trans. Power Syst., vol. 9, no. 1, pp. 128–##135, Feb. 1994.##[35] S. Sen and D. P. Kothari, “Optimal##thermal generating unit commitment :A##review,” Elect. Power Energy Syst., vol.##20, no. 7, pp. 443–451, 1998.##[36] M.R. AminNaseri, A.R. Soroush,##Combined use of unsupervised and##supervised learning for daily peak load##forecasting, Energy Conversion and##Management 49 (2008) 1302–1308. ##[37] Ramezani M, Falaghi H, Haghifam M,##Shahryari GA. Shortterm electric load##forecasting using neural networks. In:##Proceedings of the EUROCON –##international conference on computer as a##tool; Belgrade, Serbia and Montenegro,##vol. 2; 2005. p. 1525–8.##[38] Fidalgo JN, Peças Lopes JA. Load##forecasting performance enhancement##when facing anomalous events. IEEE##Trans Power Syst 2005;20(1):408–15.##[39] Santos P, Martins A, Pires A. Designing##the input vector to ANNbased models for##shortterm load forecast in electricity##distribution systems. Int J Electr Power##Energy Syst 2007;29(4):338–47.##[40] Chicco G, Napoli R, Piglione F. Load##pattern clustering for shortterm load##forecasting of anomalous days. In:##Proceedings of the IEEE PowerTech##2001, Porto, Portugal, September 10–13;##2001. p. 2.##[41] Fidalgo J, Matos M. A. forecasting##portugal global load with artificial neural##networks. In: Proceedings of the##ICANN2007 – international congress on##artificial neural networks, Porto, Portugal,##September 9–13; 2007. p. 728–37.##[42] Lamedica R, Prudenzi A, Sforna M,##Caciotta M, Cencellli V. A neural##network based technique for shortterm##forecasting of anomalous load periods.##IEEE Trans Power Syst##1996;11(4):1749–56.##[43] Danilo Bassi, Oscar Olivares, "Medium##Term Electric Load Forecasting Using##TLFN Neural Networks" International##Journal of Computers, Communications##& Control Vol. I (2006), No. 2, pp. 2332.##[44] A. G. Bakirtzis, J.B. Theocharis, S.J.##Kiartzis, K.J. Satsios, Short term load##forecasting using fuzzy neural networks,##IEEE Trans, Power Syst, 10(3) 1518##1524, 1995.##[45] A. D. Papalexopoulos et al., “An##Implementation of a Neural Network##Based Load Forecasting Model for the##EMS,” IEEE Trans. Power Systems. Vol.##9, No. 4, p. 19561962 (1994).##[46] T. Rashid et al., “A Practical Approach##for Electricity Load Forecasting,” World##Academy of Science, Engineering and##Technology (2005).##[47] A.S.Pandey et al., “Clustering based##formulation for Short Term Load##Forecasting” International Journal of##Intelligent Systems and Technologies 4:2##[48] M. A. Farhat, “Longterm industrial load##forecasting and planning usingneural##networks technique and fuzzy inference##method,” in Proceedings of the 2004##IEEE Universities Power Engineering##Conference, pp. 368– 372, 2004.##[49] Domingo A. Gundin, Celiano Garcia,##Yannis A. Dimitriadis, Eduardo Garcia,##Guillermo Vega, ShortTerm Load##Forecasting for Industrial Customers##Using FASART and FASBACK Neurofuzzy##Systems, Power Systems##Computation Conference (PSCC),##Seville, Spain, 2002.##[50] Santosh Kulkarni, Sishaj P Simon, A##New Spike Based Neural Network for##ShortTerm Electrical Load Forecasting,##Fourth International Conference on##Computational Intelligence and##Communication Networks, 2012. ##[51] K. NoseFilho, A. D. P. Lotufo, and C. R.##Minussi, “Shortterm multimodal load##forecasting in distribution systems using##general regression neural networks,”##presented at the IEEE Trondheim##PowerTech, Trondheim, Norway, Jun.##19–23, 2011.##[52] K. NoseFilho, A. D. P. Lotufo, and C. R.##Minussi, “Preprocessing data for shortterm##load forecasting with a general##regression neural network and amoving##average filter,” presented at the IEEE##Trondheim PowerTech, Trondheim,##Norway, Jun. 19–23, 2011.##[53] Kenji NoseFilho, Anna Diva Plasencia##Lotufo and Carlos Roberto Minussi ,##ShortTerm Multinodal Load Forecasting##Using a Modified General Regression##Neural Network, IEEE##TRANSACTIONS ON POWER##DELIVERY, VOL. 26, NO. 4,##OCTOBER 2011,##[54] D. F. Specht, “A generalized regression##neural network,” IEEE Trans. Neural##Netw., vol. 2, no. 6, pp. 568–576, Nov.##[55] T. Kohonen, Selforganisation and##Associative Memory, 3rd edn., Springer##Verlag, Berlin, 1989.##[56] M. Lópeza, S. Valeroa, C. Senabrea, J.##Apariciob, A. Gabaldonc, Application of##SOM neural networks to shortterm load##forecasting: The Spanish electricity##market case study, Electric Power##Systems Research 91 (2012) 18– 27.##]