An intelligent weather station

Sensors (Switzerland)

Volumn 15, Issue 12, 2015, Pages 31005-31022

  Mestre, Gonçalo ^a   Ruano, Antonio ^a,b   Duarte, Helder ^a   Silva, Sergio ^a   Khosravani, Hamid ^a   Pesteh, Shabnam ^a   Ferreira, Pedro M ^c   Horta, Ricardo ^d  

^a UNIVERSITY OF ALGARVE   (Portugal)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^c UNIVERSITY OF LISBON   (Portugal)

^d Rolear SA   (Portugal)

Author keywords

MOGA design; Neural networks; Prediction; Self powered device; Weather station; Wireless communications

Indexed keywords

ATMOSPHERIC HUMIDITY; FORECASTING; NEURAL NETWORKS; SOFTWARE DESIGN; SOFTWARE PROTOTYPING; WEATHER INFORMATION SERVICES; WIRELESS TELECOMMUNICATION SYSTEMS;

ARTIFICIAL NEURAL NETWORK MODELS; FORECASTING PERFORMANCE; GLOBAL SOLAR RADIATION; HARDWARE AND SOFTWARE DESIGNS; SELF-POWERED; TEMPERATURE AND RELATIVE HUMIDITY; WEATHER STATIONS; WIRELESS COMMUNICATIONS;

WEATHER FORECASTING;

EID: 84949844544     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s151229841     Document Type: Article

References (38)

1. Moxa Inc. Nport z3150 Series User’s Manual. Available online: http://www.moxa.com/doc/man/NPort_Z3150_Series_Users_Manual_v2.pdf (accessed on 6 December 2015).
2. Libelium. Waspmote Overview. Available online: http://www.libelium.com/products/waspmote/overview/(accessed on 6 December 2015).
3. Das, M.; Ghosh, S.K. A probabilistic approach for weather forecast using spatio-temporal inter-relationships among climate variables. In Proceedings of the 2014 9th International Conference on Industrial and Information Systems (ICIIS), Gwalior, India, 15–17 December 2014; pp. 1–6
4. Chen, S.X.; Gooi, H.B.; Wang, M.Q. Solar radiation forecast based on fuzzy logic and neural networks. Renew. Energy 2013, 60, 195–201. [CrossRef]
5. Inman, R.H.; Pedro, H.T.C.; Coimbra, C.F.M. Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 2013, 39, 535–576. [CrossRef]
6. Sharma, N.; Sharma, P.; Irwin, D.; Shenoy, P. Predicting solar generation from weather forecasts using machine learning. In Proceedings of the 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium, 17–20 October 2011; pp. 528–533
7. Ferreira, P.M.; Ruano, A.E. Discrete model based greenhouse environmental control using the branch & bound algorithm. In Proceedings of the 17th IFAC World Congress, Seoul, Korea, 6–11 July 2008
8. Luo, Y.; Chang, X.; Peng, S.; Khan, S.; Wang, W.; Zheng, Q.; Cai, X. Short-term forecasting of daily reference evapotranspiration using the hargreaves-samani model and temperature forecasts. Agric. Water Manag. 2014, 136, 42–51. [CrossRef]
9. Ferreira, P.M.; Ruano, A.E.; Silva, S.; Conceicao, E.Z.E. Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy Build. 2012, 55, 238–251. [CrossRef]
10. Collotta, M.; Messineo, A.; Nicolosi, G.; Pau, G. A dynamic fuzzy controller to meet thermal comfort by using neural network forecasted parameters as the input. Energies 2014, 7, 4727–4756. [CrossRef]
11. Renner, C.; Nguyen, P.A.T. A system for efficient dissemination of weather forecasts for sustainable solar-powered sensors. In Proceedings of the Sustainable Internet and ICT for Sustainability (SustainIT), Madrid, Spain, 14–15 April 2015; pp. 1–8
12. Ferreira, P.M.; Gomes, J.M.; Martins, I.A.C.; Ruano, A.E. A neural network based intelligent predictive sensor for cloudiness, solar radiation, and air temperature. Sensors 2012, 12, 15750–15777. [CrossRef] [PubMed]
13. IEEE. IEEE Standard for Local and Metropolitan Area Networks. Available online: http://standards.ieee.org/getieee802/download/802.15.4k-2013.pdf (accessed on 6 December 2015).
14. Foundation, R.P. Raspberry Pi—Model B. Available online: http://www.raspberrypi.org/products/model-b/(accessed on 6 December 2015).
15. Raspbian. Raspbian OS. Available online: http://www.raspbian.org/RaspbianDocumentation (accessed on 6 December 2015).
16. Debian. Debian OS. Available online: www.debian.org (accessed on 6 December 2015).
17. AG, S. Sht7x Datasheet. Available online: http://www.sensirion.com/fileadmin/user_upload/customers/sensirion/Dokumente/Humidity/Sensirion_Humidity_SHT7x_Datasheet_V5.pdf (accessed on 6 December 2015).
18. Apogee Instruments, Inc. Owner’s Manual—Pyranometer Model sp-110 and sp-230. Available online: https://www.apogeeinstruments.co.uk/content/SP-110manual.pdf (accessed on 6 December 2015).
19. Davisnet. Radiation Shiled User´s Guide. Available online: http://www.davisnet.com/product_documents/weather/manuals/07395-093_IM_07714.pdf (accessed on 6 December 2015).
20. Tecnology, L. Ltc2309 Datasheet. Available online: http://cds.linear.com/docs/en/datasheet/2309fd.pdf (accessed on 6 December 2015).
21. Inc, D.I. Xbee RF Modules. Available online: http://www.digi.com/products/wireless-wired-embeddedsolutions/zigbee-rf-modules/zigbee-mesh-module/(accessed on 6 December 2015).
22. Digi International Inc. Xbeer/Xbee-pror868 RF Modules. Available online: http://ftp1.digi.com/support/documentation/90001020_E.pdf (accessed on 6 November 2015).
23. Digi International Inc. Xbeer/Xbee-pror RF Modules. Available online: http://ftp1.digi.com/support/documentation/90000982_R.pdf (accessed on 6 November 2015).
24. Yuasa. Np 10-6 Data Sheet. Available online: http://www.yuasabatteries.com/pdfs/NP_10_6_DataSheet.pdf (accessed on 6 December 2015).
25. SunStore. 12v 20w Monocrystalline Solar Panel. Available online: http://www.sunstore.co.uk/12v-20w- Monocrystalline-Solar-Panel.html (accessed on 6 December 2015).
26. Tecnologies, L. Ltc4417 Datasheet. Available online: http://cds.linear.com/docs/en/datasheet/4417f.pdf (accessed on 6 December 2015).
27. DSM & T. Ip Rating. Available online: http://www.dsmt.com/pdf/resources/iprating.pdf (accessed on 6 December 2015).
28. Ferreira, P.M.; Pestana, R.; Ruano, A.E. Improving the identification of rbf predictive models to forecast the portuguese electricity consumption. In Proceedings of the IFAC Conference on Control Methodologies and Technology for Energy Efficiency (CMTEE 2010), Vilamoura, Portugal, 29–31 March 2010
29. Garro, B.A.; Sossa, H.; Vásquez, R. Back-propagation vs. particle swarm optimization algorithm: Which algorithm is better to adjust the synaptic weights of a feed-forward ann? Int. J. Artif. Intell. 2011, 7, 208–212
30. Nawi, N.; khan, A.; Rehman, M.Z.; Aziz, M.; Herawan, T.; Abawajy, J. An accelerated particle swarm optimization based levenberg marquardt back propagation algorithm. In Neural Information Processing; Loo, C., Yap, K.,Wong, K., Teoh, A., Huang, K., Eds.; Springer International Publishing: Berlin, Germany, 2014; Volume 8835, pp. 245–253
31. Zhang, W.; Niu, P.; Li, G.; Li, P. Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm. Knowl. Based Syst. 2013, 39, 34–44. [CrossRef]
32. Ferreira, P.; Ruano, A. Evolutionary multiobjective neural network models identification: Evolving task-optimised models. In New Advances in Intelligent Signal Processing; Ruano, A., Várkonyi-Kóczy, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; Volume 372, pp. 21–53
33. Levenberg, K. A method for the solution of certain problems in least squares. Q. Appl. Math. 1944, 2, 164–168
34. Marquardt, D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 1963, 11, 431–441. [CrossRef]
35. Ruano, A.E.B.; Jones, D.I.; Fleming, P.J. A new formulation of the learning problem for a neural network controller. In Proceedings of the 30th IEEE Conference on Decision and Control, Brighton, UK, 11–13 December 1991; pp. 865–866
36. Ferreira, P.M.; Faria, E.A.; Ruano, A.E. Neural network models in greenhouse air temperature prediction. Neurocomputing 2002, 43, 51–75. [CrossRef]
37. Haykin, S. Neural Networks: A Comprehensive Foundation, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 1999
38. Ruano, A.; Khosravani, H.R.; Ferreira, P.M. A randomized approximation convex hull algorithm for high dimensions. In Proceedings of the 2nd IFAC Conference on Embedded Systems, Computational Intelligence and Telematics Control (CESCIT), Maribor, Slovenia, 22–24 June 2015.