Regional wind monitoring system based on multiple sensor networks: A crowdsourcing preliminary test

Abstract The availability of updated information of regional wind fields is fundamental for an efficient management and prediction of wind power production. The present paper proposes a regional wind monitoring system based on the integration of different meteorological nets as a direct way to obtain this information. Concretely, we describe a monitoring system for the region of Andalusia supported by 198 stations, able to provide near surface wind field estimations with updating period of 10 min. Each of these stations has different characteristics since they are focused on measuring different environmental parameters, and most of them are below the quality level required by the World Meteorological Organization for wind measurement. Despite this drawback, the proposed monitoring system takes advantage of the high density of measurement points to produce valuable descriptions of the regional wind field. A basic geostatistical model coupled to the system achieves better results than Numerical Weather Prediction models, obtaining RMSE values of 1.52 m/s and 64.9° for speed and directional series respectively. The test confirms the tolerance of this massive monitoring system to data without quality assurance, being an interesting platform to implement crowdsourcing methodologies.

[1]  Dongsheng Chen,et al.  A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling. , 2012, Journal of environmental management.

[2]  J. Guiot,et al.  Spatial Objective Analysis With Applications in Atmospheric Sciences , 1988 .

[3]  S. Zecchetto,et al.  A comparison of WRF model simulations with SAR wind data in two case studies of orographic lee waves over the Eastern Mediterranean Sea , 2013 .

[4]  Moncho Gómez-Gesteira,et al.  A sensitivity study of the WRF model in wind simulation for an area of high wind energy , 2012, Environ. Model. Softw..

[5]  Juan José González de la Rosa,et al.  Basic meteorological stations as wind data source: A mesoscalar test , 2012 .

[6]  Adel Gastli,et al.  Nested ensemble NWP approach for wind energy assessment , 2012 .

[7]  Y. Gagnon,et al.  High resolution wind atlas for Nakhon Si Thammarat and Songkhla provinces, Thailand , 2013 .

[8]  G. Solari,et al.  Wind climate micro-zoning : a pilot application to Liguria Region (North Western Italy) , 2003 .

[9]  William P. Mahoney,et al.  The impact of model physics on numerical wind forecasts , 2013 .

[10]  N. Barton,et al.  Surface currents and winds at the Delaware Bay mouth , 2011 .

[11]  E. Migoya,et al.  Wind energy resource assessment in Madrid region , 2007 .

[12]  Juan José González de la Rosa,et al.  Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting , 2013 .

[13]  P. Drobinski,et al.  Statistical downscaling of near-surface wind over complex terrain in southern France , 2009 .

[14]  M. Gómez-Gesteira,et al.  Ocean surface wind simulation forced by different reanalyses: Comparison with observed data along the Iberian Peninsula coast , 2012 .

[15]  A. M. Razali,et al.  An analysis of wind power density derived from several wind speed density functions: The regional assessment on wind power in Malaysia , 2012 .

[16]  Juan José González de la Rosa,et al.  Spatial persistence in wind analysis , 2013 .

[17]  G. Matheron Principles of geostatistics , 1963 .

[18]  Haibo Liu,et al.  Forecasting near-surface weather conditions and precipitation in Alaska’s Prince William Sound with the PWS-WRF modeling system , 2013 .

[19]  J. Adame,et al.  A mesoscale simulation of coastal circulation in the Guadalquivir valley (southwestern Iberian Peninsula) using the WRF-ARW model , 2013 .

[20]  C. Vincent,et al.  Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications , 2011 .

[21]  C. F. Ratto,et al.  Preliminary estimate of the large-scale wind energy resource with few measurements available: The case of Montenegro , 2009 .

[22]  Giansalvo Cirrincione,et al.  Wind speed spatial estimation for energy planning in Sicily: A neural kriging application , 2008 .

[23]  H. J. Thiébaux,et al.  Spatial objective analysis : with applications in atmospheric science , 1987 .

[24]  César Angeles‐Camacho,et al.  Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights , 2010 .

[25]  Fernando González-Ladrón-de-Guevara,et al.  Towards an integrated crowdsourcing definition , 2012, J. Inf. Sci..

[26]  Abdeen Mustafa Omer,et al.  On the wind energy resources of Sudan , 2008 .

[27]  Shu-hui Cheng,et al.  Application of MM5 in China: Model evaluation, seasonal variations, and sensitivity to horizontal grid resolutions , 2011 .