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The aim of this research is to analyse and provide realistic insight that will improve the provisions and investment in wind energy as potential source of energy for electricity generation. The data used for this assessment were obtained from the central database of Nigeria Meteorological Agency Abuja, which includes average wind speed, and direction per day of the selected location, measured at the anemometer height of 10 m, over a period of eleven years (2011-2021). The maximum monthly mean wind speed was found in January and December with a value of 4.9 m/s, and annual extractable energy was found to be 347.97KWh/M2. On average, the annual wind speed through eleven years was found in the range 3.88 - 4.00 m/s. The results revealed that, the wind resource in the study area fall within range and category 3.00 which is considered as marginal or unsuitable for higher wind power development and an excellent for water pumping and small scale electricity generation.

Spatial interpolation is a commonly used technique to simulate wind speeds in areas which are devoid of such measuring devices. In this paper authors examine the applicability and efficiency of Artificial-Neural-Network (ANN) formalism aimed at interpolating wind speeds in space domain. Additionally, the effect of the correlation between the wind speed at target site and its correlated neighboring site is also examined in the present paper. Hourly wind speed data set comprising of wind speeds recorded from April 2016 to August 2018 provided by Energy Sector Management Assistance Program of World Bank is used for the study. The study is supported by including four different wind speed measuring stations in Pakistan, namely, TandoGhulamAli, Umer Kot, Sujawal and Sanghar. Best estimates from ANN model are obtained for TandoGhulamAli (MAPE= 7.37%) andworst estimates are observed forSanghar site (MAPE= 10.61%).

In this study, five numerical Weibull distribution methods, namely, the maximum likelihood method, the modified maximum likelihood method (MLM), the energy pattern factor method (EPF), the graphical method (GM), and the empirical method (EM) were explored using hourly synoptic data collected from 1985 to 2013 in the district of Maroua in Cameroon. The performance analysis revealed that the MLM was the most accurate model followed by the EPF and the GM. Furthermore, the comparison between the wind speed standard deviation predicted by the proposed models and the measured data showed that the MLM has a smaller relative error of -3.33% on average compared to -11.67% on average for the EPF and -8.86% on average for the GM. As a result, the MLM was precisely recommended to estimate the scale and shape parameters for an accurate and efficient wind energy potential evaluation.

La utilización eficaz del potencial energético del viento en una región promisoria requiere de adecuadas mediciones de su velocidad obtenidas mediante la instalación de cierto número de torres con anemómetros; estos datos se procesan y los resultados caracterizan dicho potencial. La presente investigación tiene como objetivo resolver un importante problema práctico: dada una región, determinar cuántas torres de muestreo deben ser instaladas y dónde hacerlo para que las mediciones obtenidas constituyan un sistema de datos representativo para el estudio de la velocidad del viento y del potencial energético. Se propone un procedimiento donde a partir de información previa sobre la topográfica, la rugosidad y la velocidad del viento en la región y su entorno, se pueden obtener las coordenadas de las torres mejor ubicadas sobre la base del cálculo del error probable medio de las estimaciones puntuales sobre la región.The effective use of wind energy potential in a promising region requires adequate speed measurements obtained by installing a number of towers with anemometers, these data are processed and the results characterize this potential. This research aims to solve an important practical problem: given a region, determine how many sampling towers should be installed and where to do the measurements obtained to constitute a system of representative data for the study of wind speed and energy potential. From a process where prior information on the topographical, roughness and wind speed in the region and beyond, can be obtained the coordinates best placed tower on the basis of calculating the average probable error of the point estimates of the region is proposed.

En la Parte I de este artículo fue establecido el algoritmo general de un procedimiento para el diseño del muestreo de la velocidad del viento cuyos resultados tengan la capacidad de minimizar el error probable medio durante una estimación del comportamiento espacio-temporal de la velocidad del viento. En la Parte II se describe el algoritmo de selección de puntos de muestreo y el algoritmo para el cálculo del error probable de estimación. El procedimiento para diseñarlas redes de muestreo eólico es implementado en una aplicación informática que facilita la aplicación del procedimiento a la selección de los puntos donde se ubicarán las torres anemométricas en una región promisoria del municipio Moa en la Provincia Holguín, Cuba. Los resultados obtenidos son comparados con el diseño obtenido en el año 2007 por la Empresa de Ingeniería y Proyectos de Electricidad del Ministerio de Energía y Minas de la República de Cuba.In Part I of this article was established the general algorithmof a procedure for the sampling design of the wind speed and the results have the ability to minimize the average probable error for an estimate of the conduct spatiotemporal wind speed. In Part II the selection algorithm of sampling points and the algorithm for calculating the probable error estimation is described. The procedure for the samplingdesign is implemented in an computer software that facilitates the application from the procedure to the selection of the points where theanemometer towers will be located in a promissory region of the municipality Moa in the province Holguín, Cuba. The results are compared with the design obtained in 2007 by the Empresa de Ingeniería y Proyectos de Electricidad of the Ministry of Energy and Mines of the Republic of Cuba.

WindSat, the first polarimetric radiometer on orbit, launched in January 2003, provides the promise of passive ocean wind vector retrievals on a continuous basis, simultaneous with the retrieval of many other geophysical variables such as sea surface temperature, atmospheric water vapor, cloud liquid water, and sea ice extent and concentration. WindSat also serves as risk reduction for the upcoming National Polar-orbiting Operational Environmental Satellite System (NPOESS) Conical Scanning Microwave Imager/Sounder (CMIS). Since the dependence of microwave brightness temperatures on wind direction is small relative to that of other parameters such as wind speed, wind direction retrieval relies on increasingly accurate knowledge of the ocean surface microwave emission, which depends upon surface properties such as roughness and foam due to wave breaking. Coordinated near-surface measurements of ocean surface microwave emission and air-sea interaction parameters are needed to quantify the effects of the processes mentioned above in surface emission models to improve the accuracy of wind vector retrievals. Such coordinated observations were performed during the Fluxes, Air-Sea Interaction, and Remote Sensing (FAIRS) experiment conducted on the R/P Floating Instrument Platform (FLIP) in the northeastern Pacific Ocean during the Fall of 2000. X- and Ka-band partially polarimetric radiometers were mounted at the end of the port boom of R/P FLIP to measure ocean surface emission at incidence angles of 45°, 530, and 65°. A bore-sighted video camera recorded the fractional area of foam in the field of view of the radiometers. Air-sea interaction parameters that were measured concurrently include wind speed, friction velocity, heat fluxes, and significant wave height. The measured dependence of ocean surface emissivity on wind speed and friction velocity is in good agreement with, and extends, earlier observations and empirical models based on satellite data. Concurrent radiometric measurements and fractional area foam coverage data strengthen the possibility of retrieval of sea surface foam coverage using airborne or spaceborne radiometry. The dependence of emissivity on atmospheric stability is shown to be much smaller than the dependence of emissivity on wind speed. Analysis of emissivity dependence on atmospheric stability alone was inconclusive, due to the variation in atmospheric stability with wind speed. The effect of long-wave incidence angle modulation on sea surface emissivity for near-surface measurements was found to be negligible when emissivity measurements were averaged over tens to hundreds of long waves. [ABSTRACT FROM AUTHOR]

Database-assisted design (DAD) for wind results in structures with reduced material consumption and failure risks, i.e., in improved safety and sustainability. The paper presents a DAD-compatible probabilistic procedure for estimating: (1) Wind effects corresponding to any mean recurrence interval of the wind speeds, and (2) wind-load factors. Unlike the ASCE 7 Standard and earlier structural reliability estimates, the procedure accounts for the inherent randomness in the estimation of peaks and for relevant wind-related knowledge uncertainties. On the basis of a selection of uncertainty parameters believed to be reasonable, it is concluded that: (1) The extreme wind speed distribution type significantly affects the estimates of the load factors. Our estimates, based on the Extreme Value Type III Reverse Weibull distribution, yield load factors consistent with values specified in the ASCE 7 Standard—in contrast to estimates published in the 1980’s. (2) The variability of the ratio between wind speeds averaged over different time intervals can significantly affect the estimated wind-load factor. (3) An increase in size of the extreme wind data set from, say, 30 to 50 years, has a marginal effect on load factor estimates. (4) For low-rise buildings in open terrain, wind-load factors are typically affected weakly by even large uncertainties with respect to terrain roughness length. Additional research on the effect of terrain roughness uncertainties for buildings in built-up terrain may require an expansion of the aerodynamic databases currently available. It is noted that consideration of uncertainties in isolation, as commonly performed by some analysts and wind tunnel operators, especially for peak effects estimation, can be misleading; for structural applications, uncertainties need to be considered collectively. [ABSTRACT FROM AUTHOR]

The global availability of synthetic aperture radar (SAR) wave mode data from the European Remote Sensing (ERS) satellites ERS-1 and ERS-2, as well as ENVISAT, allows for the investigation of the wind field over the ocean on a global and continuous basis. For this purpose, 27 days of ERS-2 SAR wave mode data were processed, representing a total of 34 310 imagettes of size 10 km x 5 km, available every 200 km along the satellite track. In this paper, two methods for retrieving wind speeds from SAR imagettes are presented and validated, showing the applicability of ENVISAT alike SAR wave mode data for global ocean wind retrieval. The first method is based on the well-tested empirical C-band scatterometer (SCAT) models, which describe the dependency of the normalized radar cross section (NRCS) on wind speed and direction. To apply C-band models to SAR data, the NRCS needs to be accurately calibrated. This is performed by a new efficient method utilizing a subset of colocated measurements from ERS-2 SCAT and model winds from the European Centre for Medium-Range Weather Forecast (ECMWF). SAR wind speeds are computed from the calibrated imagettes and compared to the entire set of colocated ERS-2 SCAT and ECMWF model data. Comparison to ERS-2 SCAT winds result in a correlation of 0.95 with a bias of -0.01 ms[SUP-1] and an rms error of 1.0 ms[SUP-1]. The second approach is based on neural networks (NNs), which allow the retrieval of wind speeds from uncalibrated SAR imagettes. NNs are trained using the mean intensity of ERS-2 SAR imagettes and colocated wind data from the ERS-2 SCAT and ECMWF model data. Validation of the NN-retrieved SAR wind speeds to ERS-2 SCAT and ECMWF model wind data result in a correlation of 0.96 with a bias of -0.04 ms[SUP-1] and an rms error of 0.93 ms[SUP-1]. [ABSTRACT FROM AUTHOR]