پیش‌بینی بلندمدت تقاضا در "زنجیره تامین انرژی الکتریکی صنایع سنگ آهن اسپیدان" با استفاده از شبکه عصبی عمیق و ماشین یادگیری شدید

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

2 مرکز تحقیقات ریز شبکه های هوشمند- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

3 دانشکده مهندسی برق- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

چکیده

صنایع سنگ آهن اسپیدان یکی از صنایع پر مصرف برق در زنجیره تامین انرژی الکتریکی استان اصفهان به­عنوان دومین قطب صنعتی کشور و یکی از تامین­کنندگان اصلی مواد اولیه در زنجیره تامین صنایع فولاد کشور است. برنامه‏ریزی در یک زنجیره تامین انرژی الکتریکی با ابعاد بزرگ در فضائی پر از تردید و عدم قطعیت، با پیش‏بینی تقاضای انرژی ­الکتریکی آغاز می­گردد. در این مقاله یک روش پیش­بینی 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 داده­کاوی مبتنی بر ماشین یادگیری شدید پیشنهاد شده است. داده­های 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 به­صورت 24 ساعته استخراج و استفاده شده است. داده­ها در بخشی از دوره مورد نظر ناشی از عدم تولید این صنعت در بازه مورد مطالعه منقطع بود به­طوری­که فقط 40 درصد از داده­ها دارای مقدار و 60 درصد مابقی صفر یا ناهمگون بوده­اند. این موضوع باعث نقص اطلاعات و بالا رفتن خطای پیش­بینی در بخش اول الگوریتم پیشنهادی در خروجی شبکه عصبی عمیق تا 40 درصد شد. جهت بهبود پیش­بینی و کاهش خطای ایجاد شده، با تکمیل مدل پیشنهادی با ماشـین یـادگیری شـدید، امکان ایجاد یـک مدل پیش­بینی بهبود­یافته برای انجام آموزش تحت نظارت میسر گردید. در نهایت نتایج به­دست آمده با تکنیک­های دیگری مانند ماشین بردار پشتیبان و درخت تصمیم­گیری مقایسه شده است. نتایج بهبود و کاهش خطا و افزایش قابل توجه دقت روش پیشنهادی در پیش­بینی بلند-مدت تقاضا در زنجیره تامین انرژی الکتریکی صنایع سنگ آهن اسپیدان را نشان می­دهند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Long-Term Demand Forecasting in Electrical Energy Supply Chain of Espidan Ironstone Industry using Deep Learning and Extreme Learning Machine

نویسندگان [English]

  • Sepehr Moalem 1
  • Roya M. Ahari 1
  • Ghazanfar Shahgholian 2
  • Majid Moazzami 3
  • Seyed Mohammad Kazemi 1
1 Department of Industrial Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 Smart Microgrid Research Center- Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 Department of ٍElectrical Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Wavelet Transform
  • deep learning
  • intensive learning machine
  • Support vector machine
  • Decision tree
  • demand forecasting
  • electrical supply chain
  • mean absolute percentage error
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