ORIGINAL_ARTICLE
Implementation of VlSI Based Image Compression Approach on Reconfigurable Computing System - A Survey
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,wavelet-based image compression algorithm using Discrete Wavelet Transform (DWT) based on B-splinefactorization 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.
http://jaiee.iau-ahar.ac.ir/article_514431_f0ce29e7b5eebb7bd762602bea812e9a.pdf
2014-09-01T11:23:20
2017-11-20T11:23:20
1
7
Image compression
Discrete wavelet transform
Decomposed lifting algorithm (DLA)
Huffman-coding
Shahin
Shafei
shahin_shafei@yahoo.com
true
1
AUTHOR
[1] Chao-Tsung Huang, Po-Chih Tseng And
1
Liang-Gee Chen, "VLSI Architecture for
2
Forward Discrete Wavelet Transform Based
3
on B-spline Factorization", Journal of VLSI
4
Signal Processing, 40,pp. 343-353, 2005.
5
[2] Chao-Tsung Huang, Po-Chih Tseng, and
6
Liang-Gee Chen, "Analysis and VLSI
7
Architecture for 1-D and 2-D Discrete
8
Wavelet Transform", IEEE Transactions On
9
Signal Processing, Vol. 53, No. 4, APRIL
10
[3] Xixin Cao, Qingqing Xie, Chungan Peng,
11
Qingchun Wang, Dunshan Yu, "An Efficient
12
VLSI Implementation of Distributed
13
Architecture for DWT", Multimedia Signal
14
Processing, 2006 IEEE 8th Workshop on, pp.
15
364 - 367, Oct. 2006.
16
[4] Kai Liu, Ke-Yan Wang, Yun-Song Li and
17
Cheng-Ke Wu, "A novel VLSI architecture
18
for real-time line-based wavelet transform
19
using lifting scheme", Journal of Computer
20
Science and Technology, Vol. 22, no. 5,
21
September 2007
22
[5] Wang Chao and Cao Peng, "Efficient
23
Architecture for 2-Dimensional Discrete
24
Wavelet Transform with Novel Lifting
25
Algorithm", Chinese Journal of Electronics,
26
Vol.19, No.1, Jan. 2010.
27
[6] Mohsen Amiri Farahani, Mohammad Eshghi,
28
"Implementing a New Architecture of
29
Wavelet Packet Transform on FPGA",
30
Proceedings of the 8th WSEAS International
31
Conference on Acoustics & Music: Theory &
32
Applications, Vancouver, Canada, pp.19-
33
21,June 2007.
34
[7] Maria A. Trenas, Juan Lopez, Emilio L.
35
Zapata, “FPGA Implementation of Wavelet
36
Packet transform with Reconfigurable Tree
37
Structure”, Euro micro Conference, 2000.
38
Proceedings of the 26th Volume 1, pp. 244 -
39
251,5-7Sept. 2000
40
[8] Kumar Gupta, A.; Dyer, M.; Hirsch, A.;
41
Nooshabadi, S.; Taubman, D.; "Design of a
42
single chip block coder for the EBCOT
43
engine in JPEG2000", Proceedings of the
44
48th Midwest Symposium on Circuits and
45
Systems, pp: 63 - 66, 2005.
46
[9] C. Hemasundara Rao and M. Madhavi Latha,
47
"A Novel VLSI Architecture of Hybrid
48
Image Compression Model based on
49
Reversible Blockade Transform", World
50
Academy of Science, Engineering and
51
Technology 52 2009.
52
[10] Isa Servan Uzun, Abbes Amira, "Real-time
53
2-D wavelet transform implementation for
54
HDTV compression", Real-Time Imaging 11
55
pp.151-165,2005.
56
[11] Jagadish H. Pujar, Lohit M. Kadlaskar, “A
57
New Lossless Method Of Image
58
Compression And Decompression Using
59
Huffman Coding Techniques”, Journal of
60
Theoretical and Applied Information
61
Technology, Vol. 15, No.1, 2010.
62
ORIGINAL_ARTICLE
Reinforcement Learning Based PID Control of Wind Energy Conversion Systems
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.
http://jaiee.iau-ahar.ac.ir/article_514432_fe4a9b1ce870b521c462bf74d3e2e596.pdf
2014-09-01T11:23:20
2017-11-20T11:23:20
8
15
Adaptive Control
WECS
Reinforcement learning
Mohammad Esmaeil
akbari
m-akbari@iau-ahar.ac.ir
true
1
AUTHOR
Noradin
Ghadimi
noradin.1364@gmail.com
true
2
AUTHOR
[1] K. Ogawa, N.Ymammura, M.Ishda, Study for Small
1
Size Wind Power Generating System Using
2
Switched Reluctance Generator, IEEE International
3
Conference on Industrial Technology, 2006, pp.
4
1510-1515,
5
[2] S. Manesis, Fuzzy Logic Control Development in
6
SCADA Software Frameworks, International
7
Review of Automatic Control,
8
[3] F. D. Bianchi, H. De Battista and R. J. Mantz,
9
“Wind Turbine Control Systems Principles,
10
Modeling and Gain Scheduling Design” Springer-
11
Verlag London Limited 2007.
12
[4] Miguel Angel Mayosky, Gustavo I.E.Cancelo
13
“Direct Adaptive Control of Wind Energy
14
Conversion Systems Using Gaussian Networks”
15
IEEE Trans on Neural Networks,Vol.10, pp.898-
16
906, July 1999
17
[5] M.Sedighizade, A.Rezazadeh “Adaptive PID
18
Control of Wind Energy Conversion Systems Using
19
RASP1 Mother Wavelet Basis Function Networks”
20
Proceedings of Academy of Science, Engineering
21
and Technology Vol.27, pp.269-273. February 2008
22
[6] M.Sedighizade “Nonlinear Model Identification and
23
Control of Wind Turbine Using Wavelets"
24
Proceedings of the 2005 IEEE Conference on
25
Control Applications Toronto, pp.1057-1062
26
Canada, 2005
27
[7] M. Kalantari, M. Sedighizadeh “Adaptive Self
28
Tuning Control of Wind Energy Conversion
29
Systems Using Morlet Mother Wavelet Basis
30
Functions Networks”12th Mediterranean IEEE
31
Conference on Control and Automation MED’04,
32
Kusadasi, Turkey, 2004.
33
[8] X. Zhang, D. XU and Y. LIU, “Predictive
34
Functional Control of a Doubly Fed Induction
35
Generator for Variable Speed Wind Turbines,” 5th
36
World Congress on Intelligent Control and
37
Automation, June 15- 19, Hangzhou. P.R. China,
38
[9] Damien Ernst,et al ”Power System Stability Control:
39
Reinforcement Learning Framework”IEEE
40
Transaction on Power System,Vol.19,No.1,February
41
[10] R. Bellman, ”Dynamic Programming”.
42
Princeton, NJ: Princeton Univ. Press, 1957.
43
[11] P.Puleston”Control strategies for wind energy
44
conversion systems”Ph.D.dissertation,Univ.La Plata.
45
Argentina 1997
46
ORIGINAL_ARTICLE
Training Set of Data Bin for Small Black Pixels Neighborhood Recognition of Each Boundary
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 band-dependent 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.
http://jaiee.iau-ahar.ac.ir/article_514433_3556cd5188d360081946e825ad3ea21d.pdf
2014-09-01T11:23:20
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16
19
Roya
Abdollahi
roya.abdollahi6666@yahoo.com
true
1
AUTHOR
[1] Sedghi T., “A Fast and Effective Model for
1
cyclic Analysis and its application in
2
classification” Arabian Journal for Science
3
and Engineering Vol. 38 October 2012.
4
[2] Sedghi T., Amirani C. M., Fakheri M.,”
5
Robust and Effective Framework for Image
6
Retrieval Scheme using Shift Invariant
7
Texture and Shape Features” International
8
Journal of Natural and Engineering Sciences
9
4 (1): 95-101, Vol. 3, Dec, 2010.
10
ORIGINAL_ARTICLE
Fuel Cell Voltage Control for Load Variations Using Neural Networks
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.
http://jaiee.iau-ahar.ac.ir/article_514434_9897a9ce118d79a55582a25f4a68b3b1.pdf
2014-09-01T11:23:20
2017-11-20T11:23:20
20
23
Fuel cell
Dynamic behavior
Neural networks
hydrogen
neural network controller
Zolekh
Teadadi
sh.teadadi@gmail.com
true
1
AUTHOR
Hassan
Changiziyan
true
2
AUTHOR
[1]M.R. Ashraf Khorasani, M.Zamani, S.
1
Asghari, B.fageh emani, Winter 1388,
2
"Design 5 kW fuel cell system," Journal of
3
[2]M. Samavati, et al, 1388," report on the
4
conceptual design of the project design and
5
fuel cell CHP systems 5 KW", fuel cell
6
groups, Research Center of Engineering
7
[3]N.Frkhzadarshad,S.A.Purmusavy,."Simulatio
8
n and modeling of dynamic fuel cell systems
9
for distributed generation applications", the
10
sixth conference on energy, F-PRE-272-98
11
[4] C. Tabatabai, 1385 "fuel cell technology and
12
its application in the car", Futures
13
Conference, technology eyes expansion
14
[5]H. Dilafruz, 1385, "Estimation of sediment
15
Intelligent Artificial Neural Networks",
16
Thesis, Faculty of Engineering, University of
17
[6]J.Arkart,M. Ghazanfari,1383, "Neural
18
Networks (functional principle)" University
19
of Science and Technology Iran
20
[7] V.R, V.Paul,"Neural Network Based Control
21
for PEM Fuel Cells"IOSR Journal of
22
Electronics & Communication Engineering
23
(IOSR-JECE) ISSN (e): 2278-1684 ISSN
24
(P):2320-334x, pp47-52
25
[8]M.Hatti,M.Tioursi,"Dynamic Neural Network
26
Controller Model of PEM Fuel Cell System",
27
International Journal of Hydrogen Energy,
28
34(2009)5015-5021
29
ORIGINAL_ARTICLE
Load Frequency Control in Two Area Power System Using Sliding Mode Control
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.
http://jaiee.iau-ahar.ac.ir/article_514435_e3385c15cf4d3d2f1d12004bcf4eb733.pdf
2014-09-01T11:23:20
2017-11-20T11:23:20
24
36
frequency Load control
sliding mode control
dual zone system
Milad
Babakhani Qazijahan
babakhani.milad@yahoo.com
true
1
AUTHOR
[1] Al-Musabi, Naji A. "Design of optimal
1
variable structure controllers: applications
2
to power system dynamics". Diss. Master’s
3
thesis, King Fahd University of Petroleum
4
and Minerals, Dhahran, Saudi Arabia, 2004.
5
[2] H. Bevrani, T. Hiyama, Robust
6
decentralised PI based LFC design for time
7
delaypower systems, Energy Conversion
8
and Management 49 (2) (2008) 193–204.
9
[3] Y. Rebours, D. Kirschen, M. Trotignon, S.
10
Rossignol, A survey of frequency
11
andvoltage control ancillary services—Part
12
I: Technical features, IEEETransactionson
13
Power Systems 22 (1) (2007) 350–357.
14
[4] J. Frunt, A. Jokic, W. Kling, J. Myrzik, P.
15
van den Bosch, Provision of
16
ancillaryservices for balance management
17
in autonomous networks, in: Proceedings
18
ofthe 5th International Conference on
19
European Electricity Market, EEM
20
2008,2008, pp. 1–6.
21
[5] H. Shayeghi, H. Shayanfar, A. Jalili, Load
22
frequency control strategies: a stateof-theart
23
survey for the researcher, Energy
24
Conversion and Management 50(2) (2008)
25
344–353.
26
[6] E. Cam, Application of fuzzy logic for load
27
frequency control of hydro-electricalpower
28
plants, Energy Conversion and
29
Management 48 (4) (2007) 1281–1288.
30
[7] H. Shayeghi, H. Shayanfar, Application of
31
ANN technique based on _-synthesisto load
32
frequency control of interconnected power
33
system, International Journalof Electrical
34
Power & Energy Systems 28 (7) (2006)
35
503–511.
36
[8] Venkat, Aswin N., et al. "Distributed output
37
feedback MPC for power system
38
control." Decision and Control, 2006 45th
39
IEEE Conference on. IEEE, 2006.
40
[9] S. Velusami, I. Chidambaram,
41
Decentralized biased dual mode controllers
42
forload frequency control of interconnected
43
power systems considering GDBand GRC
44
non-linearities, Energy Conversion and
45
Management 48 (5) (2007)1691–1702.
46
[10] S. Hosseini, A. Etemadi, Adaptive term
47
neuro-fuzzy inference system
48
basedautomatic generation control, Electric
49
Power Systems Research 78 (7)
50
(2008)1230–1239.
51
[11] M. Alrifai, Decentralized controllers for
52
power system load frequency
53
control,ICGST International Journal on
54
Journal of Artificial Intelligence in Electrical Engineering, Vol. 3, No. 10, September 2014
55
Automatic Control and System Engineering
56
5(2) (2005) 1–16.
57
[12] H. Shayeghi, H. Shayanfar, A. Jalili, Load
58
frequency control strategies: a stateof-theart
59
survey for the researcher, Energy
60
Conversion and Management 50(2) (2008)
61
344–353.
62
[13] Vrdoljak, Krešimir, Nedjeljko Perić, and
63
Ivan Petrović. "Sliding mode based loadfrequency
64
control in power
65
systems." Electric Power Systems
66
Research80.5 (2010): 514-527.
67
[14] V. Utkin, Sliding Modes in Control
68
Optimisation, Springer-Verlag, 1992.
69
[15] K. Vrdoljak, V. Tezak, N. Peric, A sliding
70
surface design for robust load frequency
71
control in power systems, in: Proceedings
72
of the Power Tech 2007, Lausanne,
73
Switzerland, 2007, pp. 279–284,
74
http://ewh.ieee.org/conf/ powertech.
75
[16] S. Spurgeon, Hyperplane design techniques
76
for discrete-time variable structure control
77
systems, International Journal on Control
78
55 (2) (1992) 445–456.
79
[17] T. Radosevic, K. Vrdoljak, N. Peric,
80
Optimal sliding mode controller forpower
81
system’s load-frequency control, in:
82
Proceedings of the 43rd
83
InternationalUniversities Power
84
Engineering Conference, Padova, Italy,
85
2008,pp. 1–5.
86
[18] K. Vrdoljak, N. Peric, M. Mehmedovic,
87
Optimal parameters for sliding modebased
88
load-frequency control in power systems,
89
in: Proceedings of the 10thInternational
90
Workshop on Variable Structure Systems,
91
Antalya, Turkey, 2008,pp. 331–336
92
ORIGINAL_ARTICLE
Neural Networks in Electric Load Forecasting:A Comprehensive Survey
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 short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term 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.
http://jaiee.iau-ahar.ac.ir/article_514436_40c94157e14d3cf7890e1d964fdf1bf9.pdf
2014-09-01T11:23:20
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37
50
Artificial Neural Networks (ANNs)
Load Forecasting(LF)
Short Term LF
Mid Term
LF
Long Term LF
Peak LF
Unit Commitment(UC)
Vahid
Mansouri
vahidmansouri2010@gmail.com
true
1
AUTHOR
Mohammad Esmaeil
akbari
true
2
AUTHOR
[1] SRINIVASAN, D., and LEE, M. A.,
1
1995, Survey of hybrid fuzzy neural
2
approaches to electric load forecasting.
3
Proceedings of the IEEE International
4
Conference on Systems, Man and
5
Cybernetics, Part 5, Vancouver, BC, pp.
6
4004-4008.
7
[2] K. L. Ho, Y. Y. Hsu, C. F. Chen, T. E.
8
Lee, C. C. Liang, T. S. Lai, and K. K.
9
Chen, “Short term load forecasting of
10
Taiwan power system using a knowledgebased
11
expert system,” IEEE Trans.
12
Power Systems, vol. 5, no. 4, pp. 1214–
13
1221, 1990.
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[3] S. Rahman and O. Hazim, “A generalized
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knowledge-based short-term loadforecasting
16
technique,” IEEE T. Power
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Syst, vol. 8, no. 2, pp. 508–514, 1993.
18
[4] RustumMamlook , Omar Badran,
19
EmadAbdulhadi, A fuzzy inference
20
model for short-term load forecasting,
21
Energy Policy Volume 37 issue 4, 2009.
22
[5] HUANG Jing, MA Jing, XIAO Xian-
23
Yong, Mid-Long Term Load Forecasting
24
Based on Fuzzy Optimal Theory, IEEE
25
Asia-Pacific Power and Energy
26
Engineering Conference (APPEEC),
27
[6] Juan J. Cárdenas, Luis Romeral, Antonio
28
Garcia, Fabio Andrade, Load forecasting
29
framework of electricity consumptions
30
for an Intelligent Energy Management
31
System in the user-side, Expert Systems
32
with Applications Volume 39 issue 5,
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[7] I. Moghram and S. Rahman, “Analysis
34
and evaluation of five short-term load
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forecasting techniques,” IEEE Trans.
36
Power Systems, vol. 4, no. 4, pp. 1484–
37
1491, 1989.
38
[8] Young-Min Wi, Sung-Kwan Joo, and
39
Kyung-Bin Song, Holiday Load
40
Forecasting Using Fuzzy Polynomial
41
Regression With Weather Feature
42
Selection and Adjustment, IEEE
43
Transactions on Power Systems Volume
44
27 issue 2, 2012.
45
[9] A. G. Bakirtzis, J. B. Theocharis, S. J.
46
Kiartzis, and K. J. Satsios, “Short-term
47
load forecasting using fuzzy neural
48
networks,” IEEE Trans. Power Systems,
49
vol. 10, no. 3, pp. 1518–1524, 1995.
50
[10] S.E. Papadakis, J.B. Theocharis, A.G.
51
Bakirtzis, load curve based fuzzy
52
modeling technique for short-term load
53
forecasting, Fuzzy Sets and Systems
54
Volume 135 issue 2, 2003.
55
[11] S. E. Papadakis, J. B. Theocharis, S. J.
56
Kiartzis, and A. G. Bakirtzis, “A novel
57
approach to short-term load forecasting
58
using fuzzy neural networks,” IEEE
59
Trans. Power Systems, vol. 13, no. 2, pp.
60
480–492, 1998.
61
[12] H. Mori and H. Kobayashi, “Optimal
62
fuzzy inference for short-term load
63
forecasting,” IEEE Trans. Power
64
Systems, vol. 11, no. 1, pp. 390–396,
65
[13] T. Czernichow, A. Piras, K. Imhof, P.
66
Caire, Y. Jaccard, B. Dorizzi, and A.
67
Germond, “Short term electrical load
68
forecasting with artificial neural
69
networks,” Engineering Intelligent Syst.,
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vol. 2, pp. 85–99, 1996.
71
[14] A. Khotanzad, R. Afkhami-Rohani, and
72
D. Maratukulam , “ANNSTLF—
73
Artificial neural network short-term load
74
forecaster— Generation three,” IEEE
75
Trans. Power Systems, vol. 13, no. 4, pp.
76
1413–1422, 1998.
77
[15] H. Hippert, C. Pedreira, and R. Souza,
78
“Neural networks for short-term load
79
forecasting: A review and evaluation,”
80
IEEE Trans. Power Syst., vol. 16, no. 1,
81
pp. 44–55, Feb. 2001.
82
[16] M. Kazeminejad, M. Dehghan, M. B.
83
Motamadinejad, H. Rastegar, New Short
84
Term Load Forecasting Using Multilayer
85
Perceptron, IEEE International
86
Conference on Information and
87
Automation - Colombo, Sri Lanka ,
88
2006.12.1 .
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[17] Ayca Kumluca Topalli ,Ismet Erkmen,
90
Ihsan To palli Intelligent , short-term load
91
forecasting in Turkey, Electrical Power
92
and Energy Systems 28 (2006) 437–447.
93
[18] Philippe Lauret , Eric Fock, Rija N.
94
Randrianarivony, Jean-Francois
95
Manicom-Ramsamy, Bayesian neural
96
network approach to short time load
97
forecasting, Energy Conversion and
98
Management 49 (2008) 1156–1166.
99
[19] Zhi Xiao, Shi-Jie Ye, Bo Zhong, Cai-Xin
100
Sun, BP neural network with rough set
101
for short term load forecasting, Expert
102
Systems with Applications 36 (2009)
103
273–279.
104
[20] Abbas Khosravi, Saeid Nahavandi and
105
Doug Creighton, Construction of Optimal
106
Prediction Intervals for Load Forecasting
107
Problems, IEEE TRANSACTIONS ON
108
POWER SYSTEMS, VOL. 25, NO. 3,
109
AUGUST 2010.
110
[21] Ali Deihimi, Hemen Showkati,
111
Application of echo state networks in
112
short-term electric load forecasting,
113
Energy 39 (2012) 327e340.
114
[22] Yongli Wang, Dongxiao Niu, Li Ji, Shortterm
115
power load forecasting based on
116
IVL-BP neural network technology, The
117
2nd International Conference on
118
Complexity Science & Information
119
Engineering, Systems Engineering
120
Procedia 4 (2012) 168 – 174.
121
[23] M. Lópeza, S. Valeroa, C. Senabrea, J.
122
Apariciob, A. Gabaldonc, Application of
123
SOM neural networks to short-term load
124
forecasting: The Spanish electricity
125
market case study Electric Power Systems
126
Research 91 (2012) 18– 27.
127
[24] Adiga S. Chandrashekaraa, T.
128
Ananthapadmanabhab, A.D. Kulkarnib, A
129
neuro-expert system for planning and
130
load forecasting of distribution systems,
131
Electrical Power and Energy Systems 21
132
(1999) 309–314.
133
[25] M. Ghiassi, David K. Zimbra, H. Saidane,
134
Medium term system load forecasting
135
with a dynamic artificial neural network
136
model, Electric Power Systems Research
137
76 (2006) 302–316.
138
[26] Pituk Bunnoona, Kusumal
139
Chalermyanonta, Chusak Limsakula ,
140
Mid-Term Load Forecasting: Level
141
Suitably of Wavelet and Neural Network
142
based on Factor Selection , International
143
Conference on Advances in Energy
144
Engineering, Energy procedia 14(2012),
145
[27] Arash Ghanbari, S. FaridGhaderi, M. Ali
146
Azadeh, Adaptive Neuro-Fuzzy Inference
147
System vs. Regression Based Approaches
148
for Annual Electricity Load Forecasting,
149
IEEE 2nd International Conference on
150
Computer and Automation Engineering
151
(ICCAE 2010) – Singapore.
152
[28] Shahid M. Awan, Zubair. A. Khan, M.
153
Aslam, Waqar Mahmood, Affan Ahsan,
154
Application of NARX based FFNN, SVR
155
and ANN Fitting models for long term
156
industrial load forecasting and their
157
comparison, IEEE 21st International
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