Neural Networks Ensembles for Short-Term Load Forecasting<br />Matteo De Felice, ENEA, Italy<br />Xin Yao, University of B...
Aim of this work<br />application of NN ensembles to STLF<br />Outline<br />Problem Description<br />Used Models<br />Expe...
Description<br /><ul><li>Office building located in Italy
Energy Load hourly data
Short-term forecasting (up to 24 hours)</li></ul>WHY?<br />Accurate forecasting  Effective Energy Management<br />
Data<br /><ul><li>Hourly consumption data from September to December 2009
Lighting, HVAC and appliances (commonly PCs)
Timer-controlled heating system</li></li></ul><li>Data<br /><ul><li>Occupancy data (estimated from badge readers)</li></li...
Neural Networks:1. Averaging Ensemble2. Regular Negative Correlation Learning (RNCL) Ensemble</li></ul>Why Ensembles?<br /...
SARIMA/SARIMAX<br /><ul><li>Seasonal Auto Regressive Integrated Moving Average Model</li></ul>Seasonality: 168 hours (= 1 ...
Neural Network<br /><ul><li>MLP network
64 hidden neurons
Levenberg-Marquardt Training Algorithm</li></li></ul><li>Neural Networks Ensemble<br />MLP Neural Network<br />Outputs Ave...
Neural Networks Ensemble<br />
RNCL Ensemble<br />MinimizecorrelationbetweenNeural Networks outputs<br />[Chen & Yao, IEEE Transactions on Neural Network...
Testing <br />Inputs: load past samples:<br />1 week data for testing (split in T1 and T2)<br />Mean Absolute Error (MAE) ...
Testing Error Matrix<br />
Testing Results<br />Naïve model: <br />
Introduction of external data<br />We added the following inputs:<br />Hour of the day (1-24)<br />Working day flag (0-1)<...
Introduction of external data<br />SARIMA Model (linear) doesn’t exhibit a clear improvement!.<br />
Introduction of external data<br />Neural networks (non-linear) shows a marked improvement! <br />
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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).

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

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

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