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Prediction of Natural Gas Consumption with Feed-forward and Fuzzy Neural Networks

Prediction of Natural Gas Consumption with Feed-forward and Fuzzy Neural Networks. N.H. Viet Institute of Fundamental Tech. Research Polish Academy of Sciences – Poland, J. Mańdziuk Faculty of Mathematics and Information Science Warsaw University of Technology – Poland.

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Prediction of Natural Gas Consumption with Feed-forward and Fuzzy Neural Networks

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  1. Prediction of Natural Gas Consumption with Feed-forward and Fuzzy Neural Networks N.H. Viet Institute of Fundamental Tech. Research Polish Academy of Sciences – Poland, J. Mańdziuk Faculty of Mathematics and Information Science Warsaw University of Technology – Poland.

  2. Presentation’s schedule • Introduction • Feed-forward neural networks • Fuzzy neural networks • Experimental results and conclusions N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  3. Introduction • Prediction of gas consumption is an important element in business planning. • The challenges: • the volatility of consumer profile, • the strong dependency on weather conditions, • the lack of historical data. • The purpose of this work: an application to gas load prediction using various neural network models. N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  4. Introduction • Three types of prediction: • One day (short-term) prediction, • One week (mid-term) prediction, • Four week (long-term) prediction. N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  5. Introduction • The data contains the daily gas loads and the average daily temperatures. • An overview of the data: • Seasonality • Strong dependency on temperature. N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  6. Introduction • The inputs: • Historical daily gas loads, • Average daily temperatures • Time factor (the season inputs) • For the n-day period: [t + 1, t + n], two values were used: Where: • One additional bit indicating the work day/weekend day in the case of daily prediction. N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  7. Feed-forward model • General network architecture: Previous daily loads Previous daily temperatures Time encoding N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  8. Feed-forward model • Chosen configurations: • One day prediction: 9(3+3+3)-8(3+3+2)-3-1 • One week prediction: 12(5+5+2)-10(4+4+2)-4-1 • Four week prediction: 16(7+7+2)-10(4+4+2)-4-1 N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  9. Fuzzy neural model • Why to use the fuzzy neural model?: • Impreciseness of data (only average daily temperature is available), • Fuzzy neural networks generally have a better performance, N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  10. Fuzzy neural model • Fuzzy neural network architecture: Membership layer Defuzzification layer N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  11. Fuzzy neural model • Fuzzy neural network dynamics: • Gaussian membership function: • Output: N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  12. Fuzzy neural model • FNN can be trained using the gradient-based technique. • An equivalent rule sets: N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  13. Experimental results and conclusions • Training data: from Jan. 01, 2000 to Dec. 31, 2001. • Testing data: from Jan. 01 2002 to Jul. 31, 2002. • The moving window technique was used to generate the training and the testing samples. • The following experiments were performed: • Single feed-forward network (SingleN) • Single fuzzy network (FuzzyN) • 3 feed-forward networks (3AvgN) • 3 temperature context networks (3TempN) N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  14. Experimental results and conclusions • Temperature context networks: • Divide the training set into 3 overlapping subsets (denoted by Low, Medium and High ) using the average temperature, • Train 3 types of networks with these sets independently, • Combine 3 networks into one module while testing. • Remark: training the networks within a particular context should be easier than in the entire input space. N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  15. Experimental results and conclusions N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  16. Experimental results and conclusions • An example of one week and four week prediction: N.H. Viet, J. Mańdziuk: Prediction of Natural Gas Consumption with Neural Networks

  17. Thank you for your attention

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