پیش‌بینی بار الکتریکی با بکارگیری مدل‌های ترکیبی پرسپترون‌های چندلایه و خودرگرسیون میانگین متحرک انباشته فصلی
محورهای موضوعی : انرژی های تجدیدپذیر
فاطمه چاهکوتاهی
1
(دانشجوی کارشناسی ارشد مهندسی صنایع، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه صنعتی اصفهان)
مهدی خاشعی
2
(دانشگاه صنعتی اصفهان- دانشکده مهندسی صنایع و سیستم ها-اصفهان- ایران)
کلید واژه: : مدل‌های ترکیبی, پیش‌بینی سری‌های زمانی فصلی, بار الکتریکی, پرسپترون‌های چندلایه, خودرگرسیون میانگین متحرک انباشته فصلی,
چکیده مقاله :
امروزه صرفه‌جویی در زمان و اقتصاد یک کشور نیازمند برنامه‌ریزی، تصمیم‌گیری و پیش‌بینی‌های درست و منطقی در حوزه‌های مختلف می‌باشد. یکی از این حوزه‌های مطرح در هر کشور، پیش‌بینی بار الکتریکی می‌باشد. این کالا (الکتریسیته) با توجه به اینکه قابل ذخیره‌سازی نمی‌باشد، پیش‌بینی آن با حساسیت بالاتری انجام می‌گیرد. همچنین علاوه بر غیرقابل ذخیره‌بودن، در مصرف این کالا الگوهای مختلفی دیده می‌شود که مدل‌سازی آن را با روش‌های کلاسیک دشوار می‌سازد. بنابراین نیاز به روشی است که بتواند الگوهای موجود در داده‌های مرتبط با این بازار را مدل‌سازی نماید. در این مقاله از یک روش ترکیبی موازی که مدل‌های کلاسیک خطی را با مدل‌های هوش محاسباتی ترکیب می‌کند، استفاده گردیده است. ایده اصلی مدل پیشنهادی، استفاده همزمان از مدل‌های مذکور در مدل‌سازی خطی و غیرخطی‌ای که با الگوهای فصلی همراهند، می‌باشد. همچنین نتایج نشان می‌دهد که در این روش به دلیل استفاده از یک روش وزن‌دهی مستقیم، هزینه محاسباتی مدل‌سازی آن به‌صورت قابل‌توجهی از سایر روش‌های ترکیبی موازی پایین‌تر می‌باشد.
Nowadays, saving time and economy of each country requires proper planning, decision making, and rational forecasts in different areas. One of the most well-known areas that has received a lot of attention is electricity forecasting. The features of the electricity which makes it distinguished from other commodities are the impossibility of storing it and the existence of seasonality and nonlinear and ambiguity pattern in electricity data set. These features of the electricity makes it more difficult to forecast using traditional methods. Therefore, in this paper, a parallel optimal hybrid model using seasonal linear and nonlinear methods is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of individual models in the modeling of complex systems in a structure, simultaneously. Experimental results indicate that in this method due to the use of a direct weighting method, the computational cost of modeling it is significantly lower than other parallel hybrid methods.
[1] R. Weron, A. Misiorek, "Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models", International Journal of Forecasting, Vol. 24, No. 4, pp. 744–763, Oct./Dec. 2008 (doi: 10.1016/j.ijforecast.2008.08.004).
[2] S. Saab, E. Badr, G. Nasr, “Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon”, Energy, No. 26, No. 1, pp. 1–14, Jan. 2001 (doi: 10.1016/S0360-5442(00)00049-9).
[3] J. H. Park, Y. M. Park, K. Y. Lee, “Composite modeling for adaptive short-term load forecasting”, IEEE Trans. on Power Systems, Vol. 6, No. 2, pp. 450–457, May 1991 (doi: 10.1109/59.76686).
[4] D. W. Bunn, E. D. Farmer, “Comparative models for electrical load forecasting”, John Wiley & Sons, New York, 1985.
[5] J. W. Taylor, R. Buizza, “Using weather ensemble predictions in electricity demand forecasting”, International Journal Forecasting, No. 19, No. 1, pp. 57–70, Jan./March 2003 (doi: 10.1016/S0169-2070(01)00123-6).
[6] D. Singhal, K. S. Swarup, "Electricity price forecasting using artificial neural networks”, International Journal of Electrical Power and Energy Systems, Vol. 33, No. 3, pp. 550–555, March 2011 (doi: 10.1016/j.ijepes.2010.12.009).
[7] B. Novak, "Superfast autoconfiguring artificial neural networks and their application to power systems”, Electric Power Systems Research, Vol. 35, No. 1, pp. 11–16, Oct. 1995 (doi: 10.1016/0378-7796(95)00980-9).
[8] A. M. Al-Kandari, S. A. Soliman, M. E. El-Hawary, "Fuzzy short-term electric load forecasting”, International Journal of Electrical Power & Energy Systems, Vol. 26, No. 2, pp. 111–122, 2004 (doi: 10.1016/S0142-0615(03)00069-3).
[9] J. P. Choudhury, B. Sarkar, S. K. Mukherjee, "Forecasting of engineering manpower through fuzzy associative memory neural network with ARIMA: A comparative study”, Neuro Computing, Vol. 47, pp. 241-257, Aug. 2002 (doi: 10.1016/S0925-2312(01)00590-2).
[10] P. Box, G. M. Jenkins, "Time series analysis: forecasting and control”, Holden-day Inc, San Francisco, CA, 1976.
[11] M. Khashei, M. Bijari, "A novel hybridization of artificial neural networks and ARIMA models for time series forecasting”, Applied Soft Computing, Vol. 11, No. 2, pp. 2664–2675, March 2011 (doi: 10.1016/j.asoc.2010.10.015).
[12] A. Camara W. Feixing L. Xiuqin, "Energy consumption forecasting using seasonal ARIMA with artificial neural networks models", International Journal of Business and Management, Vol. 11, No. 5, April 2016 (doi: 10.5539/ijbm.v11n5p231).
[13] N. Mohamed, M. H. Ahmad, Z. Ismail, "Short term load forecasting using double seasonal ARIMA model", Proceeding of the Regional Conference on Statistical Sciences, Vol. 10, pp. 57-73, 2010.
[14] L. J. Soares, M. C. Medeiros, "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data", International Journal of Forecasting, Vol. 24, No. 4, pp. 630-44, Oct./Dec. 2008 (doi: 10.1016/j.ijforecast.2008.08.003).
[15] M. Niu, S. Sun, J. Wu, L. Yu, J. Wang, "An innovative integrated model using the singular spectrum analysis and nonlinear multi-layer perceptron network optimized by hybrid intelligent algorithm for short-term load forecasting", Applied Mathematical Modelling, Vol. 40, No. 5-6, pp. 4079-4093, March 2016 (doi:10.1016/j.apm.2015.11.030).
[16] S. Kouhi, F. Keynia, "A new cascade NN based method to short-term load forecast in deregulated electricity market", Energy Conversion and Management, Vol. 71, pp. 76-83, July 2013 (doi: 10.1016/j.enconman.2013.03.014).
[17] H. Nie, G. Liu, X. Liu, Y. Wang, "Hybrid of ARIMA and SVMs for short-term load forecasting", Energy Procedia, Vol. 16, pp. 1455-1460, 2012 (doi: 10.1016/j.egypro.2012.01.229).
[18] Y. Hu, J. Li, M. Hong, J. Ren, R. Lin, Y. Liu, M. Liu, Y. Man, "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process", Energy, Vol. 170, pp. 1215-1227, March 2019 (doi: 10.1016/j.energy.2018.12.208).
[19] J. Zhang, Y. M. Wei, D. Li, Z. Tan, J. Zhou, "Short term electricity load forecasting using a hybrid model", Energy, Vol. 158, pp 774-781, Sept. 2018 (doi: 10.1016/j.energy.2018.06.012).
[20] W. Hong, G. Fan, "Hybrid empirical mode decomposition with support vector regression model for short term load forecasting", Energies, Vol. 12, No. 6, pp. 1-16, 2019.
[21] Z. Wu, X. Zhao, Y. Ma, X. Zhao, "A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting", Applied Energy, Vol. 237, pp. 896-909 (doi: 10.1016/j.apenergy.2019.01.046).
[22] L. Xiao, W. Shao, M. Yu, J. Ma, C. Jin, "Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting", Applied Energy, Vol. 198, pp. 203-222, July 2017 (doi: 10.1016/j.apenergy.2017.04.039).
[23] H.J. Sadaei, P.C. L. Silva, F.G. Guimarães, M.H. Lee, "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series", Energy, Vol. 175, pp. 365-377, May 2019 (doi: 10.1016/j.energy.2019.03.081).
[24] Y. Yang, Y. Chen, Y. Wang, C. Li, L. Li, "Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting", Applied Soft Computing, Vol. 49, pp. 663-675 Dec. 2016 (doi: 10.1016/j.asoc.2016.07.053).
[25] T. Minerva, I. Poli, "Building ARMA models with genetic algorithms", Lecture Notes in Computer Science., 2037, pp. 335–342, 2001 (doi: 10.1007/3-540-45365-2-35).
[26] M. Haseyama, H. Kitajima, "An ARMA order selection method with fuzzy reasoning", Signal Processing, Vol. 81, No. 6, pp. 1331–1335, June 2001 (doi: 10.1016/S0165-1684(01)00051-2).
[27] K. Smith, N. Gupta, D. Jatinder, "Neural networks in business: techniques and applications for the operations researcher", Computers and Operations Research, No. 27, No. 11-12, pp.1045-1076, Sept. 2000 (doi: 10.1016/S0305- 0548(99)00141-0).
[28] H.B. Hwarng, H. T. Ang, "A simple neural network for ARMA (p; q) time series", Omega. No. 29, PP. 319–333, 2001.
[29] H. Demuth, B. Beale, Neural Network Toolbox User Guide, The Mathworks Inc, Natick. 2004.
[30] S. Thawornwong, D. Enke, "The adaptive selection of financial and economic variables for use with artificial neural networks", Neurocomputing, Vol. 56, pp.205-232, Jan. 2004 (doi: 10.1016/j.neucom.2003.05.001).
[31] G. Nasr, E.A. Badr, C. Joun, "Back propagation neural networks for modeling gasoline consumption", Energy Conversion and Management, No. 44, No. 6, pp. 893–905, April 2003 (doi: 10.1016/S0196-8904(02)00087-0).
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