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IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
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IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting

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The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).

The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).

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  • Time Series approach. Hagan, 1987. Phi: auto-regressive operator. Theta: moving average operator. R software
  • Transcript

    • 1. Neural Networks Ensembles for Short-Term Load Forecasting
      Matteo De Felice, ENEA, Italy
      Xin Yao, University of Birmingham, UK
      Italian New Technologies, Energy and Sustainable Economic Development Agency
    • 2. Aim of this work
      application of NN ensembles to STLF
      Outline
      Problem Description
      Used Models
      Experimental Results
    • 3. Description
      • Office building located in Italy
      • 4. Energy Load hourly data
      • 5. Short-term forecasting (up to 24 hours)
      WHY?
      Accurate forecasting  Effective Energy Management
    • 6. Data
      • Hourly consumption data from September to December 2009
      • 7. Lighting, HVAC and appliances (commonly PCs)
      • 8. Timer-controlled heating system
    • Data
      • Occupancy data (estimated from badge readers)
    • Methodologies
      • Box-Jenkins Seasonal Model (SARIMA)
      • 9. Neural Networks:1. Averaging Ensemble2. Regular Negative Correlation Learning (RNCL) Ensemble
      Why Ensembles?
      Ensemble  Lower error variance (see Hansen & Salomon, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (10), 1990)
    • 10. SARIMA/SARIMAX
      • Seasonal Auto Regressive Integrated Moving Average Model
      Seasonality: 168 hours (= 1 week), see Autocorrelation Function (below)
    • 11. Neural Network
      • MLP network
      • 12. 64 hidden neurons
      • 13. Levenberg-Marquardt Training Algorithm
    • Neural Networks Ensemble
      MLP Neural Network
      Outputs Averaging
    • 14. Neural Networks Ensemble
    • 15. RNCL Ensemble
      MinimizecorrelationbetweenNeural Networks outputs
      [Chen & Yao, IEEE Transactions on Neural Networks, 20 (12), 2009]
      New errorfunction:
      Regularization term
    • 16. Testing
      Inputs: load past samples:
      1 week data for testing (split in T1 and T2)
      Mean Absolute Error (MAE) and MSE
    • 17. Testing Error Matrix
    • 18. Testing Results
      Naïve model:
    • 19. Introduction of external data
      We added the following inputs:
      Hour of the day (1-24)
      Working day flag (0-1)
      Building Occupancy
      Neural networks: added 3 additional inputs (known future assumption!)
      SARIMA becomes SARIMAX
    • 20. Introduction of external data
      SARIMA Model (linear) doesn’t exhibit a clear improvement!.
    • 21. Introduction of external data
      Neural networks (non-linear) shows a marked improvement!
    • 22. Testing Results – external data
    • 23. Forecasting – external data
    • 24. Conclusions
      • Ensemble overcomes common neural networks drawbacks (high error variance)
      • 25. Ensemble shows better exploitation of external data than SARIMAX model
    • Future work
      More advanced statistical models
      More realistic scenarios (no “known future” assumption)
      Economic Potential Value of Forecasting
      NN Ensembles on STLF Benchmark (ASHRAE)
      Data available on http://matteodefelice.name/research

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