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Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Some empirical evaluations of a temperatureforecasting module based on Artificial NeuralNetworks for a domotic home environment
F. Zamora-Martınez, P. Romeu, J. Pardo, D. Tormo
Embedded Systems and Artificial Intelligence groupDepartamento de ciencias fısicas, matematicas y de la computacion
Escuela Superior de Ensenanzas Tecnicas (ESET)Universidad CEU Cardenal Herrera, 46115 Alfara del Patriarca, Valencia (Spain)
KDIR – October 6, 2012
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Index
1 Introduction
2 Domotic home environment setup
3 Data preprocessing
4 Neural Network description
5 Experimentation
6 Conclusions and future work
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Introduction
Index
1 Introduction
2 Domotic home environment setup
3 Data preprocessing
4 Neural Network description
5 Experimentation
6 Conclusions and future work
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Introduction
SMLhouse
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Introduction
Introduction and motivation
SMLhouse is a domotic solar house project presented at theSolarDecathlon 2010.
The Computer Aided Energy Saving (CAES) system is beingdeveloped to decrease power consumption, increasing energyefficiency, keeping comfort parameters.
Indoor temperature is related with comfort and powerconsumption.
Artificial Neural Networks (ANNs) are a powerful tool for patternclassification and forecasting.
This work is an empirical experimentation to set the best ANNparameters in a real forecasting task.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Domotic home environment setup
Index
1 Introduction
2 Domotic home environment setup
3 Data preprocessing
4 Neural Network description
5 Experimentation
6 Conclusions and future work
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Domotic home environment setup
Hardware architecture
Lights,roller-shutters,HVAC, . . .
Temperature, airquality, humidity,. . .
Light Switches,dimmers, . . .
⇒ ⇒ Ethernet
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Domotic home environment setup
Software architecture
First layer: data is acquired from the KNX bus bythe Open Home Automation Bus (openHAB).
Second layer: data persistence module collectsensor and actuator values every minute.
iOS interface ANN Modules
Persistence
(REST interface)KNX-IP Bridge→ openHAB ⇐
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Domotic home environment setup
Software architecture
First layer: data is acquired from the KNX bus bythe Open Home Automation Bus (openHAB).
Second layer: data persistence module collectsensor and actuator values every minute.
iOS interface ANN Modules
Persistence ⇐(REST interface)
KNX-IP Bridge→ openHAB
Timestamp Name Value. . . . . . . . .
2011-03-30 10:51 Dinning Room Temperature 30.02011-03-30 10:52 Dinning Room Humidity 52.0
. . . . . . . . .
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Domotic home environment setup
Software architecture
Third layer: two applications that couldcommunicate between themselves. A native iOSapplication for manual control. A couple ofmodules that can actuate autonomously.
iOS interface ANN Modules ⇐
Persistence
(REST interface)KNX-IP Bridge→ openHAB
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Domotic home environment setup
Software architecture
Third layer: two applications that couldcommunicate between themselves. A native iOSapplication for manual control. A couple ofmodules that can actuate autonomously.
iOS interface ANN Modules ⇐
Persistence
(REST interface)KNX-IP Bridge→ openHAB
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Data preprocessing
Index
1 Introduction
2 Domotic home environment setup
3 Data preprocessing
4 Neural Network description
5 Experimentation
6 Conclusions and future work
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Data preprocessing
Data detailsAcquisition
The data temperature signal is a sequence s1s2 . . .sN of values,
sampled with a period of 1 minute.
Preprocessing
1 Low-pass filter (mean with 5 samples): s′1s′2 . . .s′N where
s′i = (si + si−1 + si−2 + si−3 + si−4)/5
2 Data normalized subtracting mean and dividing by the standarddeviation: s′′1s′′2 . . .s
′′N where
s′′i =s′i− s′
σ(s′)
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Data preprocessing
Dataset size
Partition Number of patterns DaysTraining 30 240 21Validation 10 080 7Test 10 080 7
Validation partition is sequential with training partition.
Test partition is one week ahead from last validation point.
Mean and standard deviation normalization values werecomputed over the training plus validation.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Data preprocessing
Plot of the dinning room temperature for validation partition
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0 2000 4000 6000 8000 10000
ºC
Time (minutes)
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Neural Network description
Index
1 Introduction
2 Domotic home environment setup
3 Data preprocessing
4 Neural Network description
5 Experimentation
6 Conclusions and future work
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Neural Network description
Neural Network description
At time step i:
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Neural Network description
Neural Network description
At time step i:the ANN input receives:
the hour component of the current time (locally encoded) anda window of the previous temperature values (α is step, and M isnumber of steps):
s′′i s′′i−αs′′i−2α . . .s′′i−(M−1)·α
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Neural Network description
Neural Network description
At time step i:
and computes a window with the next predicted temperaturevalues (L is forecast horizon):
s′′i+1s′′i+2s′′i+3 . . .s′′i+L
Known as multi-step-ahead direct forecasting.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Neural Network description
Multi-step-ahead forecasting approaches
Multi-step-ahead iterative forecasting was very extended inliterature. Only one future value is predicted and reused to predictiteratively the whole window. Better for small future horizons.
Multi-step-ahead direct forecasting approach is based on thecomputation of the future window in one step. Better for largefuture horizons.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Neural Network description
Training details
Error back-propagation algorithm with momentum term.
The ANN learn to map predicted output values (oi) withcorresponding true values (p?i ),
minimizing the MSE function
E =
MSE1
2L ∑i(oi− p?i )
2
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Neural Network description
Training details
Error back-propagation algorithm with momentum term.
The ANN learn to map predicted output values (oi) withcorresponding true values (p?i ),
minimizing the MSE function, adding weight decay L2regularization
E =
MSE1
2L ∑i(oi− p?i )
2
weight decay
+ ε ∑w∈{W HO ⋃
W IH}
w2
2
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Index
1 Introduction
2 Domotic home environment setup
3 Data preprocessing
4 Neural Network description
5 Experimentation
6 Conclusions and future work
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation: training parameters
An exhaustive exploration leads to this parameters:learning rate of 0.001,momentum of 0.0005,weight decay of 1×10−7,input window step of α = 2,input window size of M = 30,one hidden layer with 8 neurons and logistic activation function.output window horizon L experiments will be shown in detail.
The ANN best topology was (15+24)×8×L.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (II): evaluationANNs were trained modifying the output window horizon focusingresults only on L = 60,120,180 (denoted by NN–060, NN–120,NN–180).
Evaluation measures
Mean Absolute Error (MAE):
MAE =1N ∑
i|pi− p?i |
Normalized Root Mean Square Error (NRMSE):
NRMSE =
√√√√√√∑i(pi− p?i )
2
∑i(pi− p?i )
2
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (III): forecasting mean temperatures
In order to focus the temperature forecasting measured errors ontheir future use on an automatic control system, we will computethe mean (or max/min) temperature forecasted by the model inthe selected forecasting window.
Then we could measure the MAE value between this mean andthe ground truth mean on the same window.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (IV): individual models plot
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
20 40 60 80 100 120 140 160 180
MA
E
Window upper bound
NN−060NN−120NN−180
Plot of the MAE error computed over the mean of forecasting windows0–20, 0–40, 0–60, 0–80, . . . , 0–180, using ANN models trained withL = 60,120,180.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (V): ensemble of models
An ensemble of NN–060 and NN–180 model would ensure goodperformance in all cases.
A linear combination of ANN outputs was performed, following:
oi =
NN–060
osi +
NN–180
oli
2, for 0≤ i < 60 ;
NN–180
oli , for 60≤ i < 180 .
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (VI): ensemble vs individual models plot
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
20 40 60 80 100 120 140 160 180
MA
E
Window upper bound
NN−060NN−120NN−180
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
20 40 60 80 100 120 140 160 180
MA
E
Window upper bound
NN−060NN−120NN−MIX
Plot of the MAE error computed over the mean of forecasting windows0–20, 0–40, 0–60, 0–80, . . . , 0–180, using NN–060, NN–120, andNN–MIX models (right).
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (VII): validation and test set final results
NN–MIX model results for validation setWindow Min Max Mean
0–60 0.029/0.050 0.047/0.061 0.027/0.04360–120 0.068/0.115 0.099/0.135 0.079/0.122
120–180 0.129/0.214 0.165/0.233 0.143/0.223
NN–MIX model results for test setWindow Min Max Mean
0–60 0.139/0.188 0.173/0.254 0.150/0.20560–120 0.255/0.371 0.239/0.360 0.270/0.394
120–180 0.334/0.539 0.381/0.603 0.352/0.566
NRMSE/MAE on minimum, maximum, and mean temperatureforecasting for validation and test sets.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (VIII): validation set forecasting plot
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0 2000 4000 6000 8000 10000
ºC
Time (minutes)
NN−MIXGround Truth
Plot of validation set forecasted mean temperature versus ground truthmean temperature using a forecasting window of 0–60 with NN–MIXmodel.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Experimentation
Experimentation (IX): test set forecasting plot
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0 2000 4000 6000 8000 10000
ºC
Time (minutes)
NN−MIXGround Truth
Plot of test set forecasted mean temperature versus ground truth meantemperature using a forecasting window of 0–60 with NN–MIX model.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Conclusions and future work
Index
1 Introduction
2 Domotic home environment setup
3 Data preprocessing
4 Neural Network description
5 Experimentation
6 Conclusions and future work
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Conclusions and future work
Conclusions
A real hardware/software architecture was introduced for domotichome environments: SMLhouse.
Preliminary data was used for model testing and validation.
Monitoring and manual control systems are running.Intelligent control modules are being developed: dinning roomtemperature forecast module.
Promising results: little MAE error was achieved (0.6◦C for threehours forecast).It motivates the integration of this ideas into an automatic controlsystem.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Conclusions and future work
Future work
Covariate forecasting.
Extend forecasting module to air quality, humidity, powerconsumption, insolation, . . .
Introduce confidence on the prediction, based on predictionintervals.
Replace feedforward ANN with a recurrent neural network:Long-Short Term Memory.
Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment
Conclusions and future work
Questions?
Thanks for your attention!
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