Forecasting Energy Price and Consumption for Iranian Industrial Sectors Using ANN and ANFIS

10.22099/ijes.2013.2033

Abstract

Forecasting energy price and consumption is essential in making effective managerial decisions and plans. While there are many sophisticated mathematical methods developed so far to forecast, some nature-based intelligent algorithms with desired characteristics have been developed recently. The main objective of this research is short term forecasting of energy price and consumption in Iranian industrial sector using artificial intelligence including an Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Artificial Neural Networks (ANN). The dataset contains monthly price and consumption of gas oil, petrol, and liquid petroleum gas in the period between March 1996 and March 2010. Based on dataset, energy price and consumption for 2011 and 2012 are forecasted. The results obtained utilizing the two methods show that while both are appropriate tools to forecast price and consumption, most of the time ANFIS has lower error than ANN in terms of the mean squared error criterion

Keywords


Article Title [Persian]

پیش‌بینی قیمت و مصرف انرژی در بخش صنعت ایران با استفاده از شبکه عصبی مصنوعی و سیستم استنتاج تطبیقی فازی – عصبی

Abstract [Persian]

پیش‌بینی قیمت و مصرف انرژی در امر تصمیم‌گیری و برنامه‌ریزی مؤثر و کارآمد برای مدیران، نقش اساسی دارد. با وجود اینکه روش‌های آماری و ریاضی بسیاری برای پیش‌بینی وجود دارد، استفاده از الگوریتم‌های هوشمند با ویژگی‌های مطلوب در سال‌های اخیر پیشرفت قابل ملاحظه‌ای داشته است. هدف اصلی در این تحقیق پیش‌بینی قیمت و مصرف انرژی در بخش صنعت ایران با استفاده از شبکه عصبی مصنوعی و سیستم استنتاج تطبیقی عصبی فازی است. داده‌ها شامل قیمت و مصرف گازوئیل، بنزین و گاز مایع در بازه زمانی فروردین ماه 1375 تا فروردین ماه 1389 است، که بر اساس آنها مصرف و قیمت انرژی برای سال‌های 1390 و1391 پیش‌بینی شده است. نتایج مطالعه نشان می‌دهد درحالی‌که هر دو روش ذکر شده در مدل‌سازی و پیش‌بینی داده‌ها موفق‌اند، ولی با توجه به شاخص میانگین مجذور خطا، روش سیستم استنتاج تطبیقی عصبی فازی در اکثر موارد با دقت بالاتری نسبت به شبکه عصبی، داده‌ها را پیش‌بینی کرده است

Keywords [Persian]

  • پیش‌بینی
  • شبکه عصبی مصنوعی
  • سیستم استنتاج تطبیقی عصبی فازی
  • قیمت انرژی
  • مصرف انرژی
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