پیش‌بینی بلندمدت تقاضا در "زنجیره تامین انرژی الکتریکی صنایع سنگ آهن اسپیدان" با استفاده از شبکه عصبی عمیق و ماشین یادگیری شدید
محورهای موضوعی : انرژی های تجدیدپذیر
سپهر معلم
1
,
رویا محمدعلی پوراهری
2
,
غضنفر شاهقلیان
3
,
مجید معظمی
4
,
سید محمد کاظمی
5
1 - گروه مهندسی صنایع- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
2 - گروه مهندسی صنایع- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
3 - مرکز تحقیقات ریز شبکه های هوشمند- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
4 - دانشکده مهندسی برق- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
5 - گروه مهندسی صنایع- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
کلید واژه: ماشین بردار پشتیبان, تبدیل موجک, درخت تصمیم گیری, یادگیری عمیق, ماشین یادگیری شدید, زنجیره تامین انرژی الکتریکی, میانگین درصد خطا, پیش‌بینـی تقاضا,
چکیده مقاله :
صنایع سنگ آهن اسپیدان یکی از صنایع پر مصرف برق در زنجیره تامین انرژی الکتریکی استان اصفهان به عنوان دومین قطب صنعتی کشور و یکی از تامین کنندگان اصلی مواد اولیه در زنجیره تامین صنایع فولاد کشور است. برنامه ریزی در یک زنجیره تامین انرژی الکتریکی با ابعاد بزرگ در فضائی پر از تردید و عدم قطعیت، با پیش بینی تقاضای انرژی الکتریکی آغاز می گردد. در این مقاله یک روش پیش بینی بلندمدت تقاضا در زنجیره تامین انرژی الکتریکی صنایع سنگ آهن اسپیدان اصفهان با استفاده از یک روش ترکیبی شامل تبدیل موجک، شبکه عصبی عمیق و تکنیک داده کاوی مبتنی بر ماشین یادگیری شدید پیشنهاد شده است. داده های مورد نظر در این مطالعه با توجه به اطلاعات ثبت شده از سیگنال تقاضای انرژی الکتریکی صنایع تولیدی سنگ آهن اسپیدان در یک بازه زمانی 40 ماهه و به صورت 24 ساعته استخراج و استفاده شده است. داده ها در بخشی از دوره مورد نظر ناشی از عدم تولید این صنعت در بازه مورد مطالعه منقطع بود به طوری که فقط 40 درصد از داده ها دارای مقدار و 60 درصد مابقی صفر یا ناهمگون بوده اند. این موضوع باعث نقص اطلاعات و بالا رفتن خطای پیش بینی در بخش اول الگوریتم پیشنهادی در خروجی شبکه عصبی عمیق تا 40 درصد شد. جهت بهبود پیش بینی و کاهش خطای ایجاد شده، با تکمیل مدل پیشنهادی با ماشـین یـادگیری شـدید، امکان ایجاد یـک مدل پیش بینی بهبود یافته برای انجام آموزش تحت نظارت میسر گردید. در نهایت نتایج به دست آمده با تکنیک های دیگری مانند ماشین بردار پشتیبان و درخت تصمیم گیری مقایسه شده است. نتایج بهبود و کاهش خطا و افزایش قابل توجه دقت روش پیشنهادی در پیش بینی بلند-مدت تقاضا در زنجیره تامین انرژی الکتریکی صنایع سنگ آهن اسپیدان را نشان می دهند.
Espidan ironstone industries is one of the most consumed power industries in the electricity supply chain of Isfahan province as the second industrial hub of the country and one of the main suppliers of raw materials in the supply chain of the country's steel industry. Planning in a large-scale electricity supply chain, in a space full of uncertainty, is begin with electricity demand forecasting.In this paper, a hybrid long-term demand forecasting method in the electricity supply chain of Isfahan's ironstone industries using a combined data mining method including wavelet transform,deep learning and intensive learning machine is proposed. The used data in this study is according to the recorded information from the electrical energy demand signal of Espidan ironstone industries in a period of 40 months in the form of 24-hours. The data in a part of the study period due to the lack of production of this industry in some hours are interrupted. So that only 40% of the data had a value and the remaining, 60% were zero. This subject led to information deficiencies and increases the forecasting error up to 40% in the first step of the proposed algorithm. By completing the first step of the proposed model with intense learning machine (ELM) the forecasting error is reduced and it was possible to create an improved forecasting model for supervised training. Finally, simulation results are compared with other available approaches such as support vector machine and decision tree. The results show the improvement and reduction of error and a significant increase in the accuracy of the proposed method in long-term demand forecasting in the electricity supply chain of Espidan ironstone industries.
[1] M. Tan, S. Yuan, S. Li, Y. Su, H. Li, F. He, "Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning", IEEE Trans. on Power Systems, vol. 35, no. 4, pp. 2937-2948, July 2020 (doi: 10.1109/TPWRS.2019.2963109).
[2] M. Abdollahi, M. Moazzami, “Day-ahead coordination of vehicle-to-grid operation and wind power in security constraints unit commitment (SCUC)”, Journal of Intelligent Procedures in Electrical Technology, vol. 6, no. 22, pp. 49-56, Summer 2015.
[3] A. T. Eseye, M. Lehtonen, T. Tukia, S. Uimonen, R.J. Millar, "Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems". IEEE Access, vol. 7, pp. 91463-91475, 2019 (doi: 10.1109/ACCESS.2019.2924685).
[4] T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, H. Zareipour, "Energy forecasting: A review and outlook", IEEE Open Access Journal of Power and Energy, vol. 7, pp. 376-388, Oct. 2020 (doi: 10.1109/OAJPE.2020.3029979).
[5] S.H. Mozafarpoor-Khoshrodi, G. Shahgholian, "Improvement of perturb and observe method for maximum power point tracking in wind energy conversion system using fuzzy controller", Energy Equipment and Systems, vol. 4, no. 2, pp. 111-122, Autumn 2016 (doi: 10.22059/EES.2016.23031).
[6] H. Karimi, B. Fani, G. Shahgholian, “Coordinated protection scheme based on virtual impedance control for loop-based microgrids”, Journal of Intelligent Procedures in Electrical Technology, vol. 12, no. 46, pp. 15-32, Summer 2021 (in Persian).
[7] F. Keynia, G. Memarzadeh, “Short term electric load prediction based on deep neural network and wavelet transform and input selection”, Iranian Electric Industry Journal of Quality and Productivity, vol. 8, no. 2, pp. 65-74, Autumn 2019 (in Persian).
[8] M. Moazzami, S. J. Hosseini, H. Shahinzadeh, G. Gharehpetian, J. Moradi, “SCUC considering loads and wind power forecasting uncertainties using binary gray wolf optimization method”, Majlesi Journal of Electrical Engineering, vol. 12, no. 4, pp. 15-24, Dec. 2018.
[9] R. Ebrahimi, G. Shahgholian, B. Fani, “Fast islanding detection for distribution system including PV using multi-model decision tree algorithm”, Majlesi Journal of Electrical Engineering, vol. 14, no. 4, pp. 29-38, Dec. 2020.
[10] B. Fani, S. Fehresti-Sani, E. Adib, “Short-term load forecasting of distribution power system for weekdays using old data”, Journal of Intelligent Procedures in Electrical Technology, vol. 5, no. 18, pp. 25-36, Summer 2014 (in Persian).
[11] S. N. Emenike, G. Falcone, “A review on energy supply chain resilience through optimization”, Renewable and Sustainable Energy Reviews, vol. 134, Paper Number: 110088, Dec. 2020 (doi: 10.1016/j.rser.2020.110088).
[12] O. Abedinia, N. Amjady, H. Zareipour, “A new feature selection technique for load and price forecast of electrical power systems”, IEEE Trans. on Power Systems, vol. 32, no. 1, pp. 62–74, Jan. 2017 (doi: 10.1109/TPWRS.2016.2556620).
[13] H. Shayeghi, A. Ghasemi, “Modeling of multi input multi output based LSSVM for electricity price and load forecasting in smart grid with considering demand side management”, Computational Intelligence in Electrical Engineering, vol. 6, no. 4, pp. 87-106, Winter 2016 (in Persian).
[14] W.M. Lin, C. S. Tu, R. F. Yang, M. T. Tsai, “Particle swarm optimization aided least square support vector machine for load forecast with spikes”, IET Generation, Transmission and Distribution, vol. 10, no. 5, pp- 1145–1153, April 2016 (doi: 10.1049/iet-gtd.2015.0702).
[15] H. Fang, J. Ma, W. Zhang, H. Yang, F. Chen, X. Li, "Hydraulic performance optimization of pump impeller based on a joint of particle swarm algorithm and least-squares support vector regression", IEEE Access, vol. 8, pp. 203645-203654, Nov. 2020 (doi: 10.1109/ACCESS.2020.3036913).
[16] H. Jiang, Y. Zhang, E. Muljadi, J.J. Zhang, D.W. Gao, “A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization”, IEEE Trans. on Smart Grid, vol. 9, no. 4, pp. 3341–3350, July 2018 (doi: 10.1109/TSG.2016.2628061).
[17] Y. Liu, Y. Sun, D. Infield, Y. Zhao, S. Han, J. Yan, “A hybrid forecasting method for wind power ramp based on orthogonal test and support vector machine (OT-SVM)”, IEEE Trans. on Sustainable Energy, vol. 8, no. 2, pp- 451–457, April 2017 (doi: 10.1109/TSTE.2016.2604852).
[18] J. Nowotarski, R. Weron, “Computing electricity spot price prediction intervals using quantile regression and forecast averaging”, Springer Computational Statistics Journal, vol. 30, no. 3, pp- 791-803, Sept. 2015 (doi: 10.1007/s00180-014-0523-0).
[19] B. Liu, J. Nowotarski, T. Hong, R. Weron, “Probabilistic load forecasting via quantile regression averaging on sister forecasts”, IEEE Trans. on Smart Grid, vol. 8, no. 2, pp-730-737, March 2017 (doi: 10.1109/TSG.2015.2437877).
[20] Q. Liu, Y. Shen, L. Wu, J. Li, L. Zhuang, S. Wang, “A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting”, CSEE Journal of Power and Energy Systems, vol. 4, no. 2, pp: 226–237, June 2018 (doi: 10.17775/CSEEJPES.2016.00080).
[21] P. Zeng, M. Jin, “Peak load forecasting based on multi-source data and day-to-day topological network”, IET Generation, Transmission and Distribution, vol. 12, no. 6, pp- 1374–1381, March 2018 (doi: 10.1049/iet-gtd.2017.0201).
[22] H. Shi, M. Xu, R. Li, “Deep learning for household load forecasting A novel pooling deep RNN”, IEEE Trans. on Smart Grid, vol. 9, no. 5, pp. 5271-5280, Sept. 2018 (doi: 10.1109/TSG.2017.2686012).
[23] W. Kong, Z. Y. Dong, Y. Jia. D. J. Hill, Y. Xu, Y. Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network”, IEEE Trans. on Smart Grid, vol. 10, no. 1, pp. 841-851, Jan. 2019 (doi: 10.1109/TSG.2017.2753802).
[24] F. Y. Xu, X. Cun, M. Yan, H. Yuan, Y. Wang, L. L. Lai, “Power market load forecasting on neural network with beneficial correlated regularization”, IEEE Trans. on Industrial Informatics, vol. 14, no. 11, pp. 5050-5059, Nov. 2018 (doi: 10.1109/TII.2017.2789297).
[25] M. Rafiei, T. Niknam, J. Aghaei, M. Shafie Khah, J.P.S. Catalão, “Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine”, IEEE Trans. on Smart Grid, vol. 9, no. 6, pp. 6961-6971, Nov. 2018 (doi: 10.1109/TSG.2018.2807845).
[26] W. Zhang, H. Quan, D. Srinivasan, “An improved quantile regression neural network for probabilistic load forecasting”, IEEE Trans. on Smart Grid, vol. 10, no. 4, pp. 4425-4434, July 2019 (doi: 10.1109/TSG.2018.2859749).
[27] Z. Yu, Z. Niu, W. Tang, Q. Wu, “Deep learning for daily peak load forecasting– A novel gated recurrent neural network combining dynamic time warping”, IEEE Access, vol. 7, pp. 17184–17194, Jan. 2019 (doi: 10.1109/ACCESS.2019.2895604).
[28] C. Ye, Y. Ding, P. Wang, Z. Lin, “A data driven bottom-up approach for spatial and temporal electric load forecasting”, IEEE Trans. on Power Systems, vol. 34, no. 3, pp. 1966–1979, May 2019 (doi: 10.1109/TPWRS.2018.2889995).
[29] T. Ouyang, Y. He, H. Li, Z. Sun, S. Baek, “Modeling and forecasting short-term power load with copula model and deep belief network”, IEEE Trans. on Emerging Topics in Computational Intelligence, vol. 3, no. 2, pp. 127-136, April 2019 (doi: 10.1109/TETCI.2018.2880511).
[30] K. Chen, K. Chen, Q. Wang, Z. He, J. Hu, J. He, “Short-term load forecasting with deep residual networks”, IEEE Trans. on Smart Grid, vol. 10, no. 4, pp. 3943-3952, July 2019 (doi: 10.1109/TSG.2018.2844307).
[31] Z. Deng, B. Wang, Y. Xu, T. Xu, C. Liu, Z. Zhu, “Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting", IEEE Access, vol. 7, pp. 88058-88071, July 2019 (doi: 10.1109/ACCESS.2019.2926137).
[32] Y. Wang, N. Zhang, Q. Chen, D.S. Kirschen, P. Li, Q. Xia, “Data-driven probabilistic net load forecasting with high penetration of behind-the-meter PV”, IEEE Trans. on Power Systems, vol. 33, no. 3, pp. 3255-3264, May 2018 (doi: 10.1109/TPWRS.2017.2762599).
[33] L. Alfieri, P.D. Falco, "Wavelet-based decompositions in probabilistic load forecasting", IEEE Trans. on Smart Grid, vol. 11, no. 2, pp. 1367-1376, March 2020 (doi: 10.1109/TSG.2019.2937072).
[34] Y. Hong, Y. Zhou, Q. Li, W. Xu, X. Zheng, "A deep learning method for short-term residential load forecasting in smart grid", IEEE Access, vol. 8, pp. 55785-55797, 2020 (doi: 10.1109/ACCESS.2020.2981817).
[35] X. Luo, J. Sun, L. Wang, W. Wang, W. Zhao, J. Wu, J.H. Wang, "Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy", IEEE Trans. on Industrial Informatics, vol. 14, no. 11, pp. 4963-4971, Nov. 2018 (doi: 10.1109/TII.2018.2854549).
[36] A.J.R. Reis, A.P.A. Silva, "Feature extraction via multiresolution analysis for short-term load forecasting", IEEE Trans. on Power Systems, vol. 20, no. 1, pp. 189-198, Jan. 2005 (doi: 10.1109/TPWRS.2004.840380).
[37] Z.A. Bashir, M.E. El-Hawary, "Applying wavelets to short-term load forecasting using PSO-based neural networks", IEEE Trans. on Power Systems, vol. 24, no.1, pp. 20-27, Feb. 2009 (doi: 10.1109/TPWRS.2008.2008606).
[38] Y. Chen, P.B. Luh, C. Guan, Y. Zhao, L.D. Michel, M.A. Coolbeth, P.B. Friedland, S.J. Rourke, "Short-term load forecasting: Similar day-based wavelet neural networks", IEEE Trans. on Power Systems, vol. 25, no. 1, pp. 322-330, Feb. 2010 (doi: 10.1109/TPWRS.2009.2030426).
[39] S. Mujeeb, N. Javaid, M. Ilahi, Z. Wadud, F. Ishmanov, M. K. Afzal, “Deep long short-term memory: A new price and load forecasting scheme for big data in smart cities”, MDPI Sustainability, vol. 11, no. 4, Feb. 2019 (doi: 10.3390/su11040987).
[40] G. B. Huang, Q. Y. Zhu, C. K. Siew, “Extreme learning machine: Theory and applications”, Neurocomputing, vol. 70, no. 1-3, pp. 489–501, Dec. 2206 (doi: 10.1016/j.neucom.2005.12.126).
[41] H. Liu, E. Dougherty, J. Dy, K. Torkkola, E. Tuv, H. Peng, C. Ding, F. Long, M. Berens, L. Parsons, L. Yu, Z. Zhao, G Forman, “Evolving feature selection”, IEEE Intelligent Systems, vol. 20, no. 6, pp. 46–76, Dec. 2005 (doi: 10.1109/MIS.2005.105).
[42] J. Sohrabi, M. Moazzami, “Probabilistic mid-term net load forecasting considering the effect of solar power using extreme learning machine”, Computational Intelligence in Electrical Engineering, vol. 11, no. 2, pp. 59-72, Summer 2020 (in Persian) (doi: 10.22108/isee.2019.119079.1276).
[43] T.M. Hagan, B.H. Demuth, M. Beale, Neural Network Design, PWS Publishing Company, 1996.
[44] N. Amjady, A. Daraeepour, “Midterm demand prediction of electrical power systems using a new hybrid forecast technique”, IEEE Trans. on Power Systems, vol. 26, no. 2, pp. 755-765, May 2011 (doi: 10.1109/TPWRS.2010.2055902).
_||_[1] M. Tan, S. Yuan, S. Li, Y. Su, H. Li, F. He, "Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning", IEEE Trans. on Power Systems, vol. 35, no. 4, pp. 2937-2948, July 2020 (doi: 10.1109/TPWRS.2019.2963109).
[2] M. Abdollahi, M. Moazzami, “Day-ahead coordination of vehicle-to-grid operation and wind power in security constraints unit commitment (SCUC)”, Journal of Intelligent Procedures in Electrical Technology, vol. 6, no. 22, pp. 49-56, Summer 2015.
[3] A. T. Eseye, M. Lehtonen, T. Tukia, S. Uimonen, R.J. Millar, "Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems". IEEE Access, vol. 7, pp. 91463-91475, 2019 (doi: 10.1109/ACCESS.2019.2924685).
[4] T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, H. Zareipour, "Energy forecasting: A review and outlook", IEEE Open Access Journal of Power and Energy, vol. 7, pp. 376-388, Oct. 2020 (doi: 10.1109/OAJPE.2020.3029979).
[5] S.H. Mozafarpoor-Khoshrodi, G. Shahgholian, "Improvement of perturb and observe method for maximum power point tracking in wind energy conversion system using fuzzy controller", Energy Equipment and Systems, vol. 4, no. 2, pp. 111-122, Autumn 2016 (doi: 10.22059/EES.2016.23031).
[6] H. Karimi, B. Fani, G. Shahgholian, “Coordinated protection scheme based on virtual impedance control for loop-based microgrids”, Journal of Intelligent Procedures in Electrical Technology, vol. 12, no. 46, pp. 15-32, Summer 2021 (in Persian).
[7] F. Keynia, G. Memarzadeh, “Short term electric load prediction based on deep neural network and wavelet transform and input selection”, Iranian Electric Industry Journal of Quality and Productivity, vol. 8, no. 2, pp. 65-74, Autumn 2019 (in Persian).
[8] M. Moazzami, S. J. Hosseini, H. Shahinzadeh, G. Gharehpetian, J. Moradi, “SCUC considering loads and wind power forecasting uncertainties using binary gray wolf optimization method”, Majlesi Journal of Electrical Engineering, vol. 12, no. 4, pp. 15-24, Dec. 2018.
[9] R. Ebrahimi, G. Shahgholian, B. Fani, “Fast islanding detection for distribution system including PV using multi-model decision tree algorithm”, Majlesi Journal of Electrical Engineering, vol. 14, no. 4, pp. 29-38, Dec. 2020.
[10] B. Fani, S. Fehresti-Sani, E. Adib, “Short-term load forecasting of distribution power system for weekdays using old data”, Journal of Intelligent Procedures in Electrical Technology, vol. 5, no. 18, pp. 25-36, Summer 2014 (in Persian).
[11] S. N. Emenike, G. Falcone, “A review on energy supply chain resilience through optimization”, Renewable and Sustainable Energy Reviews, vol. 134, Paper Number: 110088, Dec. 2020 (doi: 10.1016/j.rser.2020.110088).
[12] O. Abedinia, N. Amjady, H. Zareipour, “A new feature selection technique for load and price forecast of electrical power systems”, IEEE Trans. on Power Systems, vol. 32, no. 1, pp. 62–74, Jan. 2017 (doi: 10.1109/TPWRS.2016.2556620).
[13] H. Shayeghi, A. Ghasemi, “Modeling of multi input multi output based LSSVM for electricity price and load forecasting in smart grid with considering demand side management”, Computational Intelligence in Electrical Engineering, vol. 6, no. 4, pp. 87-106, Winter 2016 (in Persian).
[14] W.M. Lin, C. S. Tu, R. F. Yang, M. T. Tsai, “Particle swarm optimization aided least square support vector machine for load forecast with spikes”, IET Generation, Transmission and Distribution, vol. 10, no. 5, pp- 1145–1153, April 2016 (doi: 10.1049/iet-gtd.2015.0702).
[15] H. Fang, J. Ma, W. Zhang, H. Yang, F. Chen, X. Li, "Hydraulic performance optimization of pump impeller based on a joint of particle swarm algorithm and least-squares support vector regression", IEEE Access, vol. 8, pp. 203645-203654, Nov. 2020 (doi: 10.1109/ACCESS.2020.3036913).
[16] H. Jiang, Y. Zhang, E. Muljadi, J.J. Zhang, D.W. Gao, “A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization”, IEEE Trans. on Smart Grid, vol. 9, no. 4, pp. 3341–3350, July 2018 (doi: 10.1109/TSG.2016.2628061).
[17] Y. Liu, Y. Sun, D. Infield, Y. Zhao, S. Han, J. Yan, “A hybrid forecasting method for wind power ramp based on orthogonal test and support vector machine (OT-SVM)”, IEEE Trans. on Sustainable Energy, vol. 8, no. 2, pp- 451–457, April 2017 (doi: 10.1109/TSTE.2016.2604852).
[18] J. Nowotarski, R. Weron, “Computing electricity spot price prediction intervals using quantile regression and forecast averaging”, Springer Computational Statistics Journal, vol. 30, no. 3, pp- 791-803, Sept. 2015 (doi: 10.1007/s00180-014-0523-0).
[19] B. Liu, J. Nowotarski, T. Hong, R. Weron, “Probabilistic load forecasting via quantile regression averaging on sister forecasts”, IEEE Trans. on Smart Grid, vol. 8, no. 2, pp-730-737, March 2017 (doi: 10.1109/TSG.2015.2437877).
[20] Q. Liu, Y. Shen, L. Wu, J. Li, L. Zhuang, S. Wang, “A hybrid FCW-EMD and KF-BA-SVM based model for short-term load forecasting”, CSEE Journal of Power and Energy Systems, vol. 4, no. 2, pp: 226–237, June 2018 (doi: 10.17775/CSEEJPES.2016.00080).
[21] P. Zeng, M. Jin, “Peak load forecasting based on multi-source data and day-to-day topological network”, IET Generation, Transmission and Distribution, vol. 12, no. 6, pp- 1374–1381, March 2018 (doi: 10.1049/iet-gtd.2017.0201).
[22] H. Shi, M. Xu, R. Li, “Deep learning for household load forecasting A novel pooling deep RNN”, IEEE Trans. on Smart Grid, vol. 9, no. 5, pp. 5271-5280, Sept. 2018 (doi: 10.1109/TSG.2017.2686012).
[23] W. Kong, Z. Y. Dong, Y. Jia. D. J. Hill, Y. Xu, Y. Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network”, IEEE Trans. on Smart Grid, vol. 10, no. 1, pp. 841-851, Jan. 2019 (doi: 10.1109/TSG.2017.2753802).
[24] F. Y. Xu, X. Cun, M. Yan, H. Yuan, Y. Wang, L. L. Lai, “Power market load forecasting on neural network with beneficial correlated regularization”, IEEE Trans. on Industrial Informatics, vol. 14, no. 11, pp. 5050-5059, Nov. 2018 (doi: 10.1109/TII.2017.2789297).
[25] M. Rafiei, T. Niknam, J. Aghaei, M. Shafie Khah, J.P.S. Catalão, “Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine”, IEEE Trans. on Smart Grid, vol. 9, no. 6, pp. 6961-6971, Nov. 2018 (doi: 10.1109/TSG.2018.2807845).
[26] W. Zhang, H. Quan, D. Srinivasan, “An improved quantile regression neural network for probabilistic load forecasting”, IEEE Trans. on Smart Grid, vol. 10, no. 4, pp. 4425-4434, July 2019 (doi: 10.1109/TSG.2018.2859749).
[27] Z. Yu, Z. Niu, W. Tang, Q. Wu, “Deep learning for daily peak load forecasting– A novel gated recurrent neural network combining dynamic time warping”, IEEE Access, vol. 7, pp. 17184–17194, Jan. 2019 (doi: 10.1109/ACCESS.2019.2895604).
[28] C. Ye, Y. Ding, P. Wang, Z. Lin, “A data driven bottom-up approach for spatial and temporal electric load forecasting”, IEEE Trans. on Power Systems, vol. 34, no. 3, pp. 1966–1979, May 2019 (doi: 10.1109/TPWRS.2018.2889995).
[29] T. Ouyang, Y. He, H. Li, Z. Sun, S. Baek, “Modeling and forecasting short-term power load with copula model and deep belief network”, IEEE Trans. on Emerging Topics in Computational Intelligence, vol. 3, no. 2, pp. 127-136, April 2019 (doi: 10.1109/TETCI.2018.2880511).
[30] K. Chen, K. Chen, Q. Wang, Z. He, J. Hu, J. He, “Short-term load forecasting with deep residual networks”, IEEE Trans. on Smart Grid, vol. 10, no. 4, pp. 3943-3952, July 2019 (doi: 10.1109/TSG.2018.2844307).
[31] Z. Deng, B. Wang, Y. Xu, T. Xu, C. Liu, Z. Zhu, “Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting", IEEE Access, vol. 7, pp. 88058-88071, July 2019 (doi: 10.1109/ACCESS.2019.2926137).
[32] Y. Wang, N. Zhang, Q. Chen, D.S. Kirschen, P. Li, Q. Xia, “Data-driven probabilistic net load forecasting with high penetration of behind-the-meter PV”, IEEE Trans. on Power Systems, vol. 33, no. 3, pp. 3255-3264, May 2018 (doi: 10.1109/TPWRS.2017.2762599).
[33] L. Alfieri, P.D. Falco, "Wavelet-based decompositions in probabilistic load forecasting", IEEE Trans. on Smart Grid, vol. 11, no. 2, pp. 1367-1376, March 2020 (doi: 10.1109/TSG.2019.2937072).
[34] Y. Hong, Y. Zhou, Q. Li, W. Xu, X. Zheng, "A deep learning method for short-term residential load forecasting in smart grid", IEEE Access, vol. 8, pp. 55785-55797, 2020 (doi: 10.1109/ACCESS.2020.2981817).
[35] X. Luo, J. Sun, L. Wang, W. Wang, W. Zhao, J. Wu, J.H. Wang, "Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy", IEEE Trans. on Industrial Informatics, vol. 14, no. 11, pp. 4963-4971, Nov. 2018 (doi: 10.1109/TII.2018.2854549).
[36] A.J.R. Reis, A.P.A. Silva, "Feature extraction via multiresolution analysis for short-term load forecasting", IEEE Trans. on Power Systems, vol. 20, no. 1, pp. 189-198, Jan. 2005 (doi: 10.1109/TPWRS.2004.840380).
[37] Z.A. Bashir, M.E. El-Hawary, "Applying wavelets to short-term load forecasting using PSO-based neural networks", IEEE Trans. on Power Systems, vol. 24, no.1, pp. 20-27, Feb. 2009 (doi: 10.1109/TPWRS.2008.2008606).
[38] Y. Chen, P.B. Luh, C. Guan, Y. Zhao, L.D. Michel, M.A. Coolbeth, P.B. Friedland, S.J. Rourke, "Short-term load forecasting: Similar day-based wavelet neural networks", IEEE Trans. on Power Systems, vol. 25, no. 1, pp. 322-330, Feb. 2010 (doi: 10.1109/TPWRS.2009.2030426).
[39] S. Mujeeb, N. Javaid, M. Ilahi, Z. Wadud, F. Ishmanov, M. K. Afzal, “Deep long short-term memory: A new price and load forecasting scheme for big data in smart cities”, MDPI Sustainability, vol. 11, no. 4, Feb. 2019 (doi: 10.3390/su11040987).
[40] G. B. Huang, Q. Y. Zhu, C. K. Siew, “Extreme learning machine: Theory and applications”, Neurocomputing, vol. 70, no. 1-3, pp. 489–501, Dec. 2206 (doi: 10.1016/j.neucom.2005.12.126).
[41] H. Liu, E. Dougherty, J. Dy, K. Torkkola, E. Tuv, H. Peng, C. Ding, F. Long, M. Berens, L. Parsons, L. Yu, Z. Zhao, G Forman, “Evolving feature selection”, IEEE Intelligent Systems, vol. 20, no. 6, pp. 46–76, Dec. 2005 (doi: 10.1109/MIS.2005.105).
[42] J. Sohrabi, M. Moazzami, “Probabilistic mid-term net load forecasting considering the effect of solar power using extreme learning machine”, Computational Intelligence in Electrical Engineering, vol. 11, no. 2, pp. 59-72, Summer 2020 (in Persian) (doi: 10.22108/isee.2019.119079.1276).
[43] T.M. Hagan, B.H. Demuth, M. Beale, Neural Network Design, PWS Publishing Company, 1996.
[44] N. Amjady, A. Daraeepour, “Midterm demand prediction of electrical power systems using a new hybrid forecast technique”, IEEE Trans. on Power Systems, vol. 26, no. 2, pp. 755-765, May 2011 (doi: 10.1109/TPWRS.2010.2055902).