Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment - KDIR 2012

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


Index

1 Introduction

2 Domotic home environment setup

3 Data preprocessing

4 Neural Network description

5 Experimentation

6 Conclusions and future work


Introduction

Index

1 Introduction




5 Experimentation



Introduction

SMLhouse


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.


Domotic home environment setup

Index

1 Introduction




5 Experimentation




Hardware architecture

Lights,roller-shutters,HVAC, . . .

Temperature, airquality, humidity,. . .

Light Switches,dimmers, . . .

⇒ ⇒ Ethernet



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 ⇐




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

. . . . . . . . .




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




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


Data preprocessing

Index

1 Introduction




5 Experimentation



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′)


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.


Data preprocessing

Plot of the dinning room temperature for validation partition

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26

0 2000 4000 6000 8000 10000

ºC

Time (minutes)


Neural Network description

Index

1 Introduction




5 Experimentation





At time step i:




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




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.



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.



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



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


Experimentation

Index

1 Introduction




5 Experimentation



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.


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


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.


Experimentation

Experimentation (IV): individual models plot

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0.02

0.04

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0.08

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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.


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 .


Experimentation

Experimentation (VI): ensemble vs individual models plot

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0.02

0.04

0.06

0.08

0.10

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0.14

20 40 60 80 100 120 140 160 180

MA

E

Window upper bound

NN−060NN−120NN−180

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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).


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.


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.


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.


Conclusions and future work

Index

1 Introduction




5 Experimentation




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.



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.



Questions?

Thanks for your attention!

Documents

Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment - KDIR 2012