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

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

  1. 1. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment F. Zamora-Mart´nez, P. Romeu, J. Pardo, D. Tormo ı Embedded Systems and Artificial Intelligence group ´ Departamento de ciencias f´sicas, matematicas y de la computacion ı ´ ˜ ´ Escuela Superior de Ensenanzas Tecnicas (ESET) Universidad CEU Cardenal Herrera, 46115 Alfara del Patriarca, Valencia (Spain) KDIR – October 6, 2012
  2. 2. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environmentIndex 1 Introduction 2 Domotic home environment setup 3 Data preprocessing 4 Neural Network description 5 Experimentation 6 Conclusions and future work
  3. 3. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment IntroductionIndex 1 Introduction 2 Domotic home environment setup 3 Data preprocessing 4 Neural Network description 5 Experimentation 6 Conclusions and future work
  4. 4. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment IntroductionSMLhouse
  5. 5. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment IntroductionIntroduction and motivation SMLhouse is a domotic solar house project presented at the SolarDecathlon 2010. The Computer Aided Energy Saving (CAES) system is being developed to decrease power consumption, increasing energy efficiency, keeping comfort parameters. Indoor temperature is related with comfort and power consumption. Artificial Neural Networks (ANNs) are a powerful tool for pattern classification and forecasting. This work is an empirical experimentation to set the best ANN parameters in a real forecasting task.
  6. 6. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Domotic home environment setupIndex 1 Introduction 2 Domotic home environment setup 3 Data preprocessing 4 Neural Network description 5 Experimentation 6 Conclusions and future work
  7. 7. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Domotic home environment setupHardware architecture Lights, roller-shutters, HVAC, . . . Temperature, air ⇒ ⇒ Ethernet quality, humidity, ... Light Switches, dimmers, . . .
  8. 8. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Domotic home environment setupSoftware architecture First layer: data is acquired from the KNX bus by iOS interface ANN Modules the Open Home Automation Bus (openHAB). Persistence Second layer: data persistence module collect (REST interface) sensor and actuator values every minute. KNX-IP Bridge → openHAB ⇐
  9. 9. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Domotic home environment setupSoftware architecture First layer: data is acquired from the KNX bus by iOS interface ANN Modules the Open Home Automation Bus (openHAB). Persistence ⇐ Second layer: data persistence module collect (REST interface) sensor and actuator values every minute. KNX-IP Bridge → openHAB Timestamp Name Value ... ... ... 2011-03-30 10:51 Dinning Room Temperature 30.0 2011-03-30 10:52 Dinning Room Humidity 52.0 ... ... ...
  10. 10. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Domotic home environment setupSoftware architecture iOS interface ANN Modules ⇐ Third layer: two applications that could communicate between themselves. A native iOS Persistence application for manual control. A couple of (REST interface) modules that can actuate autonomously. KNX-IP Bridge → openHAB
  11. 11. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Domotic home environment setupSoftware architecture iOS interface ANN Modules ⇐ Third layer: two applications that could communicate between themselves. A native iOS Persistence application for manual control. A couple of (REST interface) modules that can actuate autonomously. KNX-IP Bridge → openHAB
  12. 12. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Data preprocessingIndex 1 Introduction 2 Domotic home environment setup 3 Data preprocessing 4 Neural Network description 5 Experimentation 6 Conclusions and future work
  13. 13. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Data preprocessingData details Acquisition The data temperature signal is a sequence s1 s2 . . . sN of values, sampled with a period of 1 minute. Preprocessing 1 Low-pass filter (mean with 5 samples): s1 s2 . . . sN where si = (si + si−1 + si−2 + si−3 + si−4 )/5 2 Data normalized subtracting mean and dividing by the standard deviation: s1 s2 . . . sN where si − s ¯ si = σ(s )
  14. 14. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Data preprocessingDataset size Partition Number of patterns Days Training 30 240 21 Validation 10 080 7 Test 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 were computed over the training plus validation.
  15. 15. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Data preprocessingPlot of the dinning room temperature for validation partition 26 25 24 23 22 21 ºC 20 19 18 17 16 15 0 2000 4000 6000 8000 10000 Time (minutes)
  16. 16. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Neural Network descriptionIndex 1 Introduction 2 Domotic home environment setup 3 Data preprocessing 4 Neural Network description 5 Experimentation 6 Conclusions and future work
  17. 17. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Neural Network descriptionNeural Network description At time step i:
  18. 18. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Neural Network descriptionNeural Network description At time step i: the ANN input receives: the hour component of the current time (locally encoded) and a window of the previous temperature values (α is step, and M is number of steps): si si−α si−2α . . . si−(M−1)·α
  19. 19. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Neural Network descriptionNeural Network description At time step i: and computes a window with the next predicted temperature values (L is forecast horizon): si+1 si+2 si+3 . . . si+L Known as multi-step-ahead direct forecasting.
  20. 20. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Neural Network descriptionMulti-step-ahead forecasting approaches Multi-step-ahead iterative forecasting was very extended in literature. Only one future value is predicted and reused to predict iteratively the whole window. Better for small future horizons. Multi-step-ahead direct forecasting approach is based on the computation of the future window in one step. Better for large future horizons.
  21. 21. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Neural Network descriptionTraining details Error back-propagation algorithm with momentum term. The ANN learn to map predicted output values (oi ) with corresponding true values ( pi ), minimizing the MSE function MSE 1 E = ∑ (oi − pi )2 2L i
  22. 22. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Neural Network descriptionTraining details Error back-propagation algorithm with momentum term. The ANN learn to map predicted output values (oi ) with corresponding true values ( pi ), minimizing the MSE function, adding weight decay L2 regularization MSE weight decay 1 w2 E = ∑ (oi − pi )2 + ε ∑ 2L i w∈{W HO W IH } 2
  23. 23. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationIndex 1 Introduction 2 Domotic home environment setup 3 Data preprocessing 4 Neural Network description 5 Experimentation 6 Conclusions and future work
  24. 24. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation: 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.
  25. 25. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (II): evaluation ANNs were trained modifying the output window horizon focusing results only on L = 60, 120, 180 (denoted by NN–060, NN–120, NN–180). Evaluation measures Mean Absolute Error (MAE): 1 MAE = |pi − pi | N∑i Normalized Root Mean Square Error (NRMSE): ∑ (pi − pi )2 i NRMSE = ∑ ( pi − pi )2 ¯ i
  26. 26. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (III): forecasting mean temperatures In order to focus the temperature forecasting measured errors on their future use on an automatic control system, we will compute the mean (or max/min) temperature forecasted by the model in the selected forecasting window. Then we could measure the MAE value between this mean and the ground truth mean on the same window.
  27. 27. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (IV): individual models plot 0.14 NN−060 NN−120 NN−180 0.12 0.10 0.08 MAE 0.06 0.04 0.02 0.00 20 40 60 80 100 120 140 160 180 Window upper bound Plot of the MAE error computed over the mean of forecasting windows 0–20, 0–40, 0–60, 0–80, . . . , 0–180, using ANN models trained with L = 60, 120, 180.
  28. 28. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (V): ensemble of models An ensemble of NN–060 and NN–180 model would ensure good performance in all cases. A linear combination of ANN outputs was performed, following:   NN–060 NN–180  os + ol i i , for 0 ≤ i < 60 ;  oi = 2 NN–180  l oi , for 60 ≤ i < 180 . 
  29. 29. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (VI): ensemble vs individual models plot 0.14 0.14 NN−060 NN−060 NN−120 NN−120 NN−180 NN−MIX 0.12 0.12 0.10 0.10 0.08 0.08 MAE MAE 0.06 0.06 0.04 0.04 0.02 0.02 0.00 0.00 20 40 60 80 100 120 140 160 180 20 40 60 80 100 120 140 160 180 Window upper bound Window upper bound Plot of the MAE error computed over the mean of forecasting windows 0–20, 0–40, 0–60, 0–80, . . . , 0–180, using NN–060, NN–120, and NN–MIX models (right).
  30. 30. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (VII): validation and test set final results NN–MIX model results for validation set Window Min Max Mean 0–60 0.029/0.050 0.047/0.061 0.027/0.043 60–120 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 set Window Min Max Mean 0–60 0.139/0.188 0.173/0.254 0.150/0.205 60–120 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 temperature forecasting for validation and test sets.
  31. 31. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (VIII): validation set forecasting plot 26 NN−MIX 25 Ground Truth 24 23 22 21 ºC 20 19 18 17 16 15 0 2000 4000 6000 8000 10000 Time (minutes) Plot of validation set forecasted mean temperature versus ground truth mean temperature using a forecasting window of 0–60 with NN–MIX model.
  32. 32. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment ExperimentationExperimentation (IX): test set forecasting plot 30 NN−MIX Ground Truth 28 26 ºC 24 22 20 18 0 2000 4000 6000 8000 10000 Time (minutes) Plot of test set forecasted mean temperature versus ground truth mean temperature using a forecasting window of 0–60 with NN–MIX model.
  33. 33. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Conclusions and future workIndex 1 Introduction 2 Domotic home environment setup 3 Data preprocessing 4 Neural Network description 5 Experimentation 6 Conclusions and future work
  34. 34. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Conclusions and future workConclusions A real hardware/software architecture was introduced for domotic home 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 room temperature forecast module. Promising results: little MAE error was achieved (0.6◦ C for three hours forecast). It motivates the integration of this ideas into an automatic control system.
  35. 35. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Conclusions and future workFuture work Covariate forecasting. Extend forecasting module to air quality, humidity, power consumption, insolation, . . . Introduce confidence on the prediction, based on prediction intervals. Replace feedforward ANN with a recurrent neural network: Long-Short Term Memory.
  36. 36. Some empirical evaluations of a temperature forecasting module based on Artificial Neural Networks for a domotic home environment Conclusions and future workQuestions? Thanks for your attention!
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