Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes

Sensors (Switzerland)

Volumn 15, Issue 4, 2015, Pages 9277-9304

  Pardo, Juan ^a   Zamora Martínez, Francisco ^a   Botella Rocamora, Paloma ^a  

^a UNIVERSIDAD CEU CARDENAL HERRERA   (Spain)

Author keywords

Ambient intelligence; Artificial neural networks; Energy efficiency; On line Back Propagation; Wireless sensor networks

Indexed keywords

AIR CONDITIONING; ALGORITHMS; APPLICATION SPECIFIC INTEGRATED CIRCUITS; AUTOMATION; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; COSTS; E-LEARNING; ENERGY EFFICIENCY; FORECASTING; INTELLIGENT BUILDINGS; LEARNING ALGORITHMS; MICROPROCESSOR CHIPS; NEURAL NETWORKS; SENSOR NODES; SOCIAL NETWORKING (ONLINE); SPACE HEATING; SYSTEM-ON-CHIP; TIME SERIES;

AMBIENT INTELLIGENCE; COMPUTATIONAL DEMANDING ALGORITHMS; COST MICROCONTROLLER; HISTORICAL DATABASE; INDOOR TEMPERATURE; ONLINE LEARNING ALGORITHMS; TIME SERIES FORECASTING; TIME SERIES LEARNING;

WIRELESS SENSOR NETWORKS;

EID: 84928128181     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150409277     Document Type: Article

References (24)

1. Zheng, J.; Jamalipour, A. Wireless Sensor Networks: A Networking Perspective; John Wiley & Sons: New York, NY, USA, 2009.
2. Ferreira, P.; Ruano, A.; Silva, S.; Conceição, E. Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy Build. 2012, 55, 238-251.
3. Álvarez, J.; Redondo, J.; Camponogara, E.; Normey-Rico, J.; Berenguel, M.; Ortigosa, P. Optimizing building comfort temperature regulation via model predictive control. Energy Build. 2012, 57, 361-372.
4. Wu, C.L.; Chau, K.W.; Li, Y.S. Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour. Res. 2009, 45, doi:10.1029/2007WR006737.
5. Taormina, R.; wing Chau, K.; Sethi, R. Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng. Appl. AI 2012, 25, 1670-1676.
6. Karatasou, S.; Santamouris, M.; Geros, V. Modeling and predicting building's energy use with artificial neural networks: Methods and results. Energy Build. 2006, 38, 949-958.
7. Ruano, A.; Crispim, E.; Conceição, E.; Lúcio, M. Prediction of building's temperature using neural networks models. Energy Build. 2006, 38, 682-694.
8. Zamora-Martínez, F.; Romeu, P.; Botella-Rocamora, P.; Pardo, J. Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis. Energies 2013, 6, 4639-4659.
9. Cho, S.; Zaheer-uddin, M. Predictive control of intermittently operated radiant floor heating systems. Energy Convers. Manag. 2003, 44, 1333-1342.
10. Frandina, S.; Gori, M.; Lippi, M.; Maggini, M.; Melacci, S. Variational Foundations of Online Backpropagation. In Artificial Neural Networks and Machine Learning-ICANN 2013; Springer: Sofia, Bulgaria, 2013; Volume 8131, pp. 82-89.
11. Calvet, S.; Campelo, J.C.; Estellés, F.; Perles, A.; Mercado, R.; Serrano, J.J. Suitability Evaluation of Multipoint Simultaneous CO2 Sampling Wireless Sensors for Livestock Buildings. Sensors 2014, 14, 10479-10496.
12. Dargie, W.; Poellabauer, C. Fundamentals of Wireless Sensor Networks: Theory and Practice; John Wiley & Sons: New York, NY, USA, 2010.
13. Sohraby, K.; Minoli, D.; Znati, T. Wireless Sensor Networks: Technology, Protocols, and Applications; John Wiley & Sons: New York, NY, USA, 2007.
14. Texas Instruments. CC1110F32. Low-Power So C (System-on-Chip) with MCU, Memory, Sub-1 GHz RF Transceiver, and USB Controller. Available online: http://www.ti.com/product/cc1110f32 (accessed on 9 April 2015).
15. Chatfield, C. The Analysis of Time Series: An Introduction; CRC Press: Boca Raton, FL, USA, 2013.
16. Anderson, T.W. The Statistical Analysis of Time Series; John Wiley & Sons: New York, NY, USA, 2011; Volume 19.
17. Montgomery, D.C.; Jennings, C.L.; Kulahci, M. Introduction to Time Series Analysis and Forecasting; John Wiley & Sons: New York, NY, USA, 2011; Volume 526.
18. Rumelhart, D.E.; Hinton, G.E.;Williams, R.J. Neurocomputing: Foundations of Research; Chapter Learning Representations by Back-Propagating Errors; Nature: San Francisco, CA, USA, 1988; pp. 696-699.
19. Brown, M.; Harris, C. Neurofuzzy Adaptive Modelling and Control; Prentice Hall International (UK) Ltd.: Hertfordshire, UK, 1994.
20. Gelman, A.; Carlin, J.B.; Stern, H.S.; Dunson, D.B.; Vehtari, A.; Rubin, D.B. Bayesian Data Analysis; CRC Press: Boca Raton, FL, USA, 2013.
21. Solar Decathlon Europe 2012. Available online: http://www.sdeurope.org/ (accessed on 17 April 2015).
22. Zamora-Martínez, F.; Romeu-Guallart, P.; Pardo, J. SML2010 Data Set. Available online: https://archive.ics.uci.edu/ml/datasets/SML2010 (accessed on 17 April 2015).
23. Bache, K.; Lichman, M. UCI Machine Learning Repository. Available online: http://archive.ics. uci.edu/ml/ (accessed on 17 April 2015).
24. Zamora-Martínez, F.; Romeu, P.; Botella-Rocamora, P.; Pardo, J. On-line learning of indoor temperature forecasting models towards energy efficiency. Energy Build. 2014, 83, 162-172.