پیش‌بینی بلندمدت تقاضا در "زنجیره تامین انرژی الکتریکی صنایع سنگ آهن اسپیدان" با استفاده از شبکه عصبی عمیق و ماشین یادگیری شدید
الموضوعات :
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
الکلمات المفتاحية: ماشین بردار پشتیبان, تبدیل موجک, درخت تصمیم گیری, یادگیری عمیق, ماشین یادگیری شدید, زنجیره تامین انرژی الکتریکی, میانگین درصد خطا, پیش‌بینـی تقاضا,
ملخص المقالة :
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.
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_||_[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).
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[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).
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[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).
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[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).
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[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).
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[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).
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