IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
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Transcript of IEEE SSCI 2011 Talk - Neural Networks Ensembles for Short-Term Load Forecasting
Neural Networks Ensembles for Short-Term Load ForecastingMatteo De Felice, ENEA, Italy
Xin Yao, University of Birmingham, UK
Italian New Technologies, Energy and
Sustainable Economic Development Agency
Aim of this work
1. Problem Description
2. Used Models
3. Experimental Results
application of NN ensembles to STLF
Outline
Description
Office building located in Italy
Energy Load hourly data
Short-term forecasting (up to 24 hours)
WHY?Accurate forecasting Effective Energy
Management
Data Hourly consumption data from September to
December 2009
Lighting, HVAC and appliances (commonly PCs)
Timer-controlled heating system
Data Occupancy data (estimated from badge readers)
Methodologies
Box-Jenkins Seasonal Model (SARIMA) 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)
SARIMA/SARIMAX
Seasonal Auto Regressive Integrated Moving Average Model
Seasonality: 168 hours (= 1 week), see Autocorrelation Function (below)
Neural Network
MLP network 64 hidden neurons Levenberg-Marquardt Training
Algorithm
Neural Networks Ensemble
MLP Neural Network
Outputs Averaging
Neural Networks Ensemble
RNCL Ensemble
Minimize correlation between Neural Networks outputs
[Chen & Yao, IEEE Transactions on Neural Networks, 20 (12), 2009]
New error function:
Regularization term
Testing Inputs: load past samples:
1 week data for testing (split in T1 and T2) Mean Absolute Error (MAE) and MSE
Testing Error Matrix
Testing Results
Model MSE – T1 MSE – T2
Naive 7.61 6.4
SARIMA 5.52 2.17
MLP Average 10.9 (17.88) 21.67 (59.29)
MLP Ensemble 2.95 2.4
RNCL 3.34 2.82
Naïve model:
Introduction of external dataWe added the following inputs:
1. Hour of the day (1-24)
2. Working day flag (0-1)
3. 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!
Testing Results – external data
Model MSE – T1 MSE – T2
Naive 7.61 6.4
SARIMA 5.61 2.07
MLP Average 12.13 (16.80) 11.61 (10.61)
MLP Ensemble 3.30 1.27
RNCL 2.71 1.62
(5.52) (2.17)
10.9 (17.88) 21.67 (59.29)
(2.95) (2.4)
(3.34) (2.82)
Forecasting – external data
Conclusions
Ensemble overcomes common neural networks drawbacks (high error variance)
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