پیش‌بینی بار الکتریکی با بکارگیری مدل‌های ترکیبی پرسپترون‌های چندلایه و خودرگرسیون میانگین متحرک انباشته فصلی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی صنایع، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه صنعتی اصفهان

2 دانشگاه صنعتی اصفهان- دانشکده مهندسی صنایع و سیستم ها-اصفهان- ایران

چکیده

امروزه صرفه‌جویی در زمان و اقتصاد یک کشور نیازمند برنامه‌ریزی، تصمیم‌گیری و پیش‌بینی‌های درست و منطقی در حوزه‌های مختلف می‌باشد. یکی از این حوزه‌های مطرح در هر کشور، پیش‌بینی بار الکتریکی می‌باشد. این کالا (الکتریسیته) با توجه به اینکه قابل ذخیره‌سازی نمی‌باشد، پیش‌بینی آن با حساسیت بالاتری انجام می‌گیرد. همچنین علاوه بر غیرقابل ذخیره‌بودن، در مصرف این کالا الگوهای مختلفی دیده می‌شود که مدل‌سازی آن را با روش‌های کلاسیک دشوار می‌سازد. بنابراین نیاز به روشی است که بتواند الگوهای موجود در داده‌های مرتبط با این بازار را مدل‌سازی نماید. در این مقاله از یک روش ترکیبی موازی که مدل‌های کلاسیک خطی را با مدل‌های هوش محاسباتی ترکیب می‌کند، استفاده گردیده است. ایده اصلی مدل پیشنهادی، استفاده همزمان از مدل‌های مذکور در مدل‌سازی خطی و غیرخطی‌ای که با الگوهای فصلی همراهند، می‌باشد. همچنین نتایج نشان می‌دهد که در این روش به دلیل استفاده از یک روش وزن‌دهی مستقیم، هزینه محاسباتی مدل‌سازی آن به‌صورت قابل‌توجهی از سایر روش‌های ترکیبی موازی پایین‌تر می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Electricity load forecasting using hybrid models based on Multi-Layer Perceptrons Neural Network and Seasonal Auto-Regressive Integrated Moving Average models

نویسندگان [English]

  • Fateme Chahkoutahi 1
  • Mehdi Khashei 2
1 Department of Industrial and Systems Engineering, Isfahan University of Technology
2 Department of Industrial and Systems Engineering, Isfahan University of Technology, isfahan, iran
چکیده [English]

Nowadays, saving time and economy of each country requires proper planning, decision making, and rational forecasts in different areas. One of the most well-known areas that has received a lot of attention is electricity forecasting. The features of the electricity which makes it distinguished from other commodities are the impossibility of storing it and the existence of seasonality and nonlinear and ambiguity pattern in electricity data set. These features of the electricity makes it more difficult to forecast using traditional methods. Therefore, in this paper, a parallel optimal hybrid model using seasonal linear and nonlinear methods is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of individual models in the modeling of complex systems in a structure, simultaneously. Experimental results indicate that in this method due to the use of a direct weighting method, the computational cost of modeling it is significantly lower than other parallel hybrid methods.

کلیدواژه‌ها [English]

  • Hybrid methods
  • Seasonal Time Series forecasting
  • electricity load
  • multi-layer perceptrons
  • Seasonal Auto-Regressive Integrated Moving Average models
 
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