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

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

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
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

Testing Error Matrix

Testing Results
Naïve model:

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

SARIMA Model (linear) doesn’t exhibit a clear improvement!.

Neural networks (non-linear) shows a marked improvement!

Testing Results – external data

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

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

by matteodefelice on Apr 15, 2011

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