تعداد نشریات | 418 |
تعداد شمارهها | 9,997 |
تعداد مقالات | 83,560 |
تعداد مشاهده مقاله | 77,801,265 |
تعداد دریافت فایل اصل مقاله | 54,843,900 |
Neural Networks in Electric Load Forecasting:A Comprehensive Survey | ||
journal of Artificial Intelligence in Electrical Engineering | ||
مقاله 6، دوره 3، شماره 10، آذر 2014، صفحه 37-50 اصل مقاله (471.71 K) | ||
نویسندگان | ||
Vahid Mansouri؛ Mohammad Esmaeil akbari | ||
چکیده | ||
Review and classification of electric load forecasting (LF) techniques based on artificial neural networks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANN oriented 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 in LF, (5) ANNs in Special applications of LF. The major research articles for each category are briefly described and the related literature reviewed. Conclusions are made on future research directions. | ||
کلیدواژهها | ||
Artificial Neural Networks (ANNs)؛ Load Forecasting(LF)؛ Short Term LF؛ Mid Term LF؛ Long Term LF؛ Peak LF؛ Unit Commitment(UC) | ||
مراجع | ||
[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. 4004-4008. [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 knowledge-based short-term loadforecasting technique,” IEEE T. Power Syst, vol. 8, no. 2, pp. 508–514, 1993. [4] RustumMamlook , Omar Badran, EmadAbdulhadi, A fuzzy inference model for short-term load forecasting, Energy Policy Volume 37 issue 4, 2009. [5] HUANG Jing, MA Jing, XIAO Xian- Yong, Mid-Long Term Load Forecasting Based on Fuzzy Optimal Theory, IEEE Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2011. [6] Juan J. Cárdenas, Luis Romeral, Antonio Garcia, Fabio Andrade, Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side, Expert Systems with Applications Volume 39 issue 5, 2012. [7] I. Moghram and S. Rahman, “Analysis and evaluation of five short-term load forecasting techniques,” IEEE Trans. Power Systems, vol. 4, no. 4, pp. 1484– 1491, 1989. [8] Young-Min Wi, Sung-Kwan Joo, and Kyung-Bin 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, “Short-term 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 short-term 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 short-term 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 short-term load forecasting,” IEEE Trans. Power Systems, vol. 11, no. 1, pp. 390–396, 1996. [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. Afkhami-Rohani, and D. Maratukulam , “ANNSTLF— Artificial neural network short-term 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 short-term 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 , short-term load forecasting in Turkey, Electrical Power and Energy Systems 28 (2006) 437–447. [18] Philippe Lauret , Eric Fock, Rija N. Randrianarivony, Jean-Francois Manicom-Ramsamy, Bayesian neural network approach to short time load forecasting, Energy Conversion and Management 49 (2008) 1156–1166. [19] Zhi Xiao, Shi-Jie Ye, Bo Zhong, Cai-Xin 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 short-term electric load forecasting, Energy 39 (2012) 327e340. [22] Yongli Wang, Dongxiao Niu, Li Ji, Shortterm power load forecasting based on IVL-BP 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 short-term 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 neuro-expert 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 , Mid-Term Load Forecasting: Level Suitably of Wavelet and Neural Network based on Factor Selection , International Conference on Advances in Energy Engineering, Energy procedia 14(2012), 438-444. [27] Arash Ghanbari, S. FaridGhaderi, M. Ali Azadeh, Adaptive Neuro-Fuzzy 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] Kwang-Ho Kim, Hyoung-Sun Youn and Yong-Cheol Kang, Short-Term 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, MAY 2000. [30] V.S. Kodogiannis, E.M. Anagnostakis, Soft computing based techniques for short-term load forecasting, Fuzzy Sets and Systems 128 (2002) 413–426. [31] NimaAmjady, Farshid Keynia, Mid-term load forecasting of power systems by a new prediction method, Energy Conversion and Management, 49 (2008) 2678–2687. [32] Titti Saksornchai, Wei-Jen Lee, Kittipong Methaprayoon, James R. Liao and Richard J. Ross, Improve the Unit Commitment Scheduling by Using the Neural-Network-Based Short-Term Load Forecasting, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 41, NO. 1, JANUARY/FEBRUARY 2005. [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. Amin-Naseri, 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. Short-term 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 ANN-based models for short-term 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 short-term 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 short-term 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. 23-32. [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. 1956-1962 (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 (2009). [48] M. A. Farhat, “Long-term 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, Short-Term 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 Short-Term Electrical Load Forecasting, Fourth International Conference on Computational Intelligence and Communication Networks, 2012. [51] K. Nose-Filho, A. D. P. Lotufo, and C. R. Minussi, “Short-term 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. Nose-Filho, 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 Nose-Filho, Anna Diva Plasencia Lotufo and Carlos Roberto Minussi , Short-Term 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. 1991. [55] T. Kohonen, Self-organisation 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 short-term load forecasting: The Spanish electricity market case study, Electric Power Systems Research 91 (2012) 18– 27. | ||
آمار تعداد مشاهده مقاله: 1,174 تعداد دریافت فایل اصل مقاله: 2,476 |