پیش‌بینی کوتاه‌مدت بار الکتریکی با استفاده از مدل‌های خاکستری بهبودیافته مبتنی بر تکرار

نویسندگان

دانشکده فنی و مهندسی - دانشگاه محقق اردبیلی - اردبیل

چکیده

با افزایش تقاضای مصرف انرژی الکتریکی، شناسایی روند تغییرات بار از مسائل حائز اهمیت در شبکه‌های برق می‌باشد. در این میان پیش‌بینی کوتاه‌مدت بار برای اطمینان از تأمین تقاضا و امنیت شبکه یکی از حیاتی‌ترین مباحث در مدیریت فنی و اقتصادی صنعت برق به‌شمار می‌آید. تاکنون روش‌های متعددی با دقت‌های متفاوت به‌منظور مدل‌سازی و پیش‌بینی بار در کوتاه‌مدت ارائه شده است. اکثر این روش‌ها از تعداد داده‌های زیاد و پارامترهایی غیر از متغیر پیش‌بینی استفاده می‌کنند. در این مقاله، مدل خاکستری و مدل خاکستری غلتان که می‌تواند با استفاده از تعداد داده‌های کم و با دقت بالا برای مدل‌سازی و پیش‌بینی سری زمانی استفاده شود، بهبود داده شده است. برای افزایش دقت مدل‌های پیشنهادی، روش اصلاح باقی‌مانده به‌روش فوریه به‌کار گرفته شده است. علاوه بر این، عملکرد روش‌های پیشنهادی با چهار روش دیگر با اعمال آنها به شبکه‌های ایران و نیواینگلند مقایسه شده است. چندین تعریف خطا به‌عنوان معیارهای توانایی و دقت انتخاب‌شده و حساسیت روش‌های پیشنهادی به تعداد داده‌های مورد نیاز و اندازه گام پیش‌بینی بررسی شده است. نتایج شبیه‌سازی عملکرد و دقت بالای مدل‌های پیشنهادی در مدل‌سازی و پیش‌بینی بار را نشان می‌دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Short-Term Electric Load Forecasting using Iteration Based Modified Grey Models

نویسندگان [English]

  • K. Javanajdadi
  • S. J. Seyed Shenava
  • A. Dejamkhooy
Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
چکیده [English]

Regarding to increase electric power demand consumption, identification of its change circumstances is an important issue in electric networks. From this point of view, short-term load forecasting is vital problem in technical and economic management of the power industry to ensure supply demand and network security. So far numerous methods with different accuracy have been proposed to model and forecast electric load in short-term. Most of them utilize large amounts of data and other parameters of the predictor variable. In this paper, grey model )GM(1,1)) and rolling grey model (RGM) can model and forecast time series by using low number of data and high accuracy improved. Fourier residual correction grey model (FGM) has been employed to increase the accuracy of proposed methods. In addition, the proposed methods performances have been compared with four other methods by applying them on Iran and New England networks. Several error definitions have been adopted as ability and accuracy criteria. Also, the sensitivity of the proposed methods to the number of required data and prediction step size has been investigated. Simulation results show high performance and accuracy of the proposed methods in the modeling and load forecasting.

کلیدواژه‌ها [English]

  • Short-term load forecasting
  • grey model
  • rolling grey model
  • Fourier residual correction grey model

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