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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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

Aim of this work application of NN ensembles to STLF Outline Problem Description Used Models Experimental Results

Short-term forecasting (up to 24 hours)WHY? Accurate forecasting  Effective Energy Management

Lighting, HVAC and appliances (commonly PCs)

Timer-controlled heating system,[object Object],[object Object]

Neural Networks:1. Averaging Ensemble2. Regular Negative Correlation Learning (RNCL) EnsembleWhy Ensembles? Ensemble  Lower error variance (see Hansen & Salomon, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (10), 1990)

SARIMA/SARIMAX ,[object Object],Seasonality: 168 hours (= 1 week), see Autocorrelation Function (below)

Levenberg-Marquardt Training Algorithm,[object Object]

RNCL Ensemble MinimizecorrelationbetweenNeural Networks outputs [Chen & Yao, IEEE Transactions on Neural Networks, 20 (12), 2009] New errorfunction: Regularization term

Testing Inputs: load past samples: 1 week data for testing (split in T1 and T2) Mean Absolute Error (MAE) and MSE

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

Introduction of external data SARIMA Model (linear) doesn’t exhibit a clear improvement!.

Introduction of external data Neural networks (non-linear) shows a marked improvement!

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Short Term Load Forecasting (STLF) can predict load from several minutes to week plays the vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, unable this algorithm to accurately predict future loads. This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

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

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

4. Energy Load hourly data

5. Short-term forecasting (up to 24 hours)WHY? Accurate forecasting  Effective Energy Management

7. Lighting, HVAC and appliances (commonly PCs)

9. Neural Networks:1. Averaging Ensemble2. Regular Negative Correlation Learning (RNCL) EnsembleWhy Ensembles? Ensemble  Lower error variance (see Hansen & Salomon, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (10), 1990)

10.

11.

12. 64 hidden neurons

13.

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.

25.

Hinweis der Redaktion

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

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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