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.
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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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