Short-Term Load Forecasting in Air-Conditioned
Non-Residential Buildings
th
20 International Symposium on Industrial Electronics
(ISIE 2011)
Yoseba K. Penya, Cruz E. Borges,
Denis Agote and Iván Fernández
{yoseba.penya,cruz.borges,denis.agote,ivan.fernandez}@deusto.es
27-30 June 2011, Gdańsk, Poland.
Objective
Methodology
Avoid non-linearity using the work schedule to
classify the model of the load curve.
Adjust several forecasting algorithms to every model.
Post-processes output to correct bias, reduce error or
to smooth the forecast.
Make a two days ahead forecast of the energy
consumption in a non-residential building without
automatic HVAC.
The method must be simple and scale up to
hundreds of buildings.
Load Curve Example
Model Prediction
Algorithm1
..
Algorithmn
10
Figure:
15
20
5
10
15
20
5
10
15
20
10
15
20
5
10
15
20
400
100
200
300
400
200
100
200
5
Sunday
300
400
Saturday
300
400
200
300
400
300
200
300
200
5
Friday
100
Post-Process
Thursday
100
Data Base
Wednesday
100
Real Data
100
200
300
400
Forecasted Data
Tuesday
400
Monday
100
Flow-Chart
5
10
15
20
5
10
15
20
Typical behaviour of the load curve. Error bars denotes ±σ.
Forecast Post-process
Model Prediction
K-Means: grouping by load index.
Raw information: from the work schedule.
Raw information performs much better.
Adjust Algorithms
Algorithm used: Moving Average, ARIMA,
Polynomial, Neural Networks and
SVM.
Parameter optimization: Grid search.
Moving Average is the best method.
Experimental Results
Datasets from Competitions: EUNITE and
ASHRAE.
Datasets from Buildings: same building in
two periods of time.
Results: MAPE emulating the normal
commission environment.
Y. Penya, C.E. Borges, and I. Fernández. “Short-Term Load
Forecasting in Non-Residential Buildings”. In: Proceedings of
the 10th IEEE Region 8 Conference (AFRICON). 2011 in
press.
Adjust Algorithms
Algorithm Donosti1 Donosti2 Ashrae Eunite
M. Average 7.34
13.78
5.76
6.69
SVM
7.92
14.25
5.88
7.34
Polynomial 11.91
19.78
6.94
7.36
NN
13.46
17.64
6.63
7.78
Y. Penya, C.E. Borges, D. Agote, and I. Fernandez.
“Short-term load forecasting in air-conditioned
non-residential Buildings”. In: Proceedings of the 20th IEEE
International Symposium on Industrial Electronics (ISIE).
2011 in press.
Correct bias: Add measured noise.
Classification: Automatic Rules, Bayesian
Networks, Neural Networks and SVM.
Consolidate: Error-relative Linear
Combination.
Parameter optimization: Grid search.
Only works when all forecasting algorithms
have a similar error.
Forecast Post-process
Algorithm Donosti1 Donosti2 Ashrae Eunite
Bias C.
7.51
13.76
5.86
6.66
Rule-based
8.42
14.99
5.87
7.74
Bayes Net 13.54
16.78
6.72
8.3
Meta-NN
7.34
13.78
5.61
8.15
Meta-SVM
7.42
13.79
5.61
7.21
ERLC
7.93
14.11
5.6
7.09
I. Fernández, C.E. Borges, and Y. Penya. “Efficient Building
Load Forecasting”. In: Proceedings of the 16th IEEE
International Conference on Emerging Technologies and
Factory Automation (EFTA). 2011 in press.