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Forecasting of Energy Price for Industrial Consumption Using Intelligent Algorithms

Mirsoltani, Mercede | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 43696 (51)
  4. University: Sharif University of Technology
  5. Department: Science and Engineering
  6. Advisor(s): Akhavan Niaki, Mohammas Taghi
  7. Abstract:
  8. Forecasting of energy price and consumption is essential in making managerial decisions and plans effectively and efficiently. It is a valuable technique in economic and/or engineering decisions. While there are many sophisticated mathematical methods developed so far to forecast prices and consumption, nature-based intelligent algorithms have been developed recently. Generally, high accuracy, quick responding, and ability to solve complicated models that are some desired characteristics of artificial intelligence algorithms help decision-makers to come up with good solutions to their problems. The main objective of this research is short term forecasting the energy price and consumption in the industrial sector using artificial intelligence including an Adaptive Neuro-Fuzzy Inference System and an Artificial Neural Networks Algorithm. The result of this research shows that both algorithms are appropriate tools to forecast actual data on the price and consumption. But, the results indicated that the Adaptive Neuro Fuzzy Inference System model predicted data in most of the cases has less error than the Artificial Neural Networks model.

  9. Keywords:
  10. Forecasting ; Artificial Neural Network ; Artificial Intelligence ; Adaptive Neuro-Fuzzy Inference System (ANFIS) ; Energy Consumption ; Intelligent Algorithms ; Energy Price

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