Monthly Mean Wind Speed Research Articles

A new E‐OBS gridded dataset for daily mean wind speed over Europe

AbstractIn this paper we present daily mean wind speed as a new variable in the publicly accessible E‐OBS gridded datasets. Each of the variables in the E‐OBS dataset is provided at a spatial resolution of 0.1° × 0.1° and 0.25° × 0.25°. The variable “daily mean wind speed” starts from 1980 and is updated every month. The main method that we apply is the use of adaptive covariate selection to model the monthly mean wind speed as a function of covariates, like altitude, distance to coast and surface roughness. Then, we use Gaussian process regression to interpolate the daily anomalies from the station locations to the grid locations. In addition, we develop ensemble dispersion improvement auto‐tune (EDIT) to improve the reliability of the local ensemble spread. In order to communicate the methodological uncertainty in the resulting dataset, the E‐OBS wind speed grid is provided as a 20‐member ensemble of random realizations. As part of a preliminary analysis, we investigate the hypothesized wind speed stilling effect in recent decades. Indeed, we do find a small yet significant wind speed stilling effect in the E‐OBS dataset.

Spatiotemporal characteristics and abrupt changes of wind speeds in the Guangdong–Hong Kong–Macau Greater Bay Area

Wind energy is a clean and pollution-free renewable energy source, particularly in coastal areas with abundant wind resources. To effectively plan for and invest in wind power development, accurate estimates of wind power (based on variable wind speeds) are needed. It is necessary to obtain a reliable quantification and spatiotemporal characteristics of wind speeds to inform wind energy development. In this study, the long-term wind speed data from 1973 to 2020 at 11 meteorological observation stations along the coast of the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) were analyzed using the Wind Rose, Mann–Kendall (MK) test, and Empirical Orthogonal Functions (EOF). The outcomes of this study further the understanding of the spatiotemporal distribution and variability of wind speeds at monthly, interannual, and long-term time scales along the GBA. First, the distributions of prevailing wind directions influenced by the East Asian monsoon and temperature changes are mainly between the east southeast and north directions. Second, the monthly mean wind speed (MWS) has increased slightly and steadily over the last 10 years. Third, the MWS of a particular month over a long period (CMWS) was stronger in spring and winter and followed a 6-month variation cycle. Fourth, the yearly MWS differed significantly at different locations within the GBA, with a period of approximately 20 years. In addition, there was an upward trend in the yearly MWS at each station. Lastly, for extreme climates, the yearly maximum values (YMVs) of wind speeds were spatially different and not very stable year-round, and the YMVs changed downward slightly over a long period. Overall, these findings and results show that the GBA coastal area is a suitable location for wind energy development and wind farm building.

Optimized hybrid ensemble technique for CMIP6 wind data projections under different climate-change scenarios. Case study: United Kingdom

Wind energy resources will be impacted by climate change. A novel hybrid ensemble technique is presented to improve long-term wind speed projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) data from global climate models. The technique constructs an optimized system, which relies on a Genetic Algorithm and an Enhanced Colliding Bodies Optimization technique. Next, the performance of the proposed method over a target area (United Kingdom) is evaluated between 1950 and 2014. Finally, to avoid single-valued deterministic projections and mitigate the uncertainties, the improved wind speed data series are investigated considering different climate-change scenarios – the Shared Socioeconomic Pathways (SSPs) – for the period 2015–2050. The performance of different CMIP6 models is found to differ over time and space. In the target area the data derived from the Hybrid model confirm that extreme wind events will occur more frequently. The monthly mean wind speed is expected to increase from 3.41 m/s during 1950–2014 to 3.60, 3.63, 3.48, 3.59 and 3.61 m/s during the study period in the SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5 climate-change scenarios, respectively. More generally, the results prove that the Hybrid model is highly effective in improving the accuracy, direction and geographical patterns of the data, and this novel method can narrow the potential uncertainties of numerical simulations.

Assessment of CMIP6 Model Performance for Wind Speed in China

As the major renewable energy, wind can greatly reduce carbon emissions. Following the “carbon neutral” strategy, wind power could help to achieve the realization of energy transformation and green development. Based on ERA5 reanalysis data and the multi-ensemble historical and scenario simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a variety of statistical analyses are used to evaluate the performance of CMIP6 simulating the wind speed in China. The conclusions are as follows: spatial patterns of the nine CMIP6 models are similar with ERA5, but BCC-CSM2-MR and MRI-ESM2-0 highly overestimate the wind speed in northwest China. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM behave better than the other six CMIP6 models in four specific regions are chosen for detailed study. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM tend to simulate a larger wind speed than ERA5 except the yearly averaged wind speed in region II and region IV. CESM2-WACCM and NorESM2-MM simulate a large monthly mean wind speed, but the value is relatively close with ERA5 in the summer. HadGEM3-GC31-MM overestimates wind speed in region I and region II from April to October, but gets closer with ERA during winter. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM simulate an increasing trend in Tibetan Plateau and Xinjiang in the next 100 years, while NorESM2-MM projects rising wind speed in the eastern part of Inner Mongolia, and HadGEM3-GC31-MM simulates increasing wind speed in the northeast and central China. The future wind speed in three models is projected to decline in region I, and the value of HadGEM3-GC31-MM is much larger. In region II, wind speed simulated by three models is projected to decrease, but the wind speed from HadGEM3-GC31-MM in region III and modeled wind speed in region IV from NorESM2-MM would climb with the slope equal to 0.0001 and 0.0012, respectively. This study indicates that the CMIP6 models have certain limitations to perform realistic wind changes, but CMIP6 could provide available reference for the projection of wind in specific areas.

Foredune formation and evolution on a prograding sea-breeze dominated beach

This study investigates the mechanisms of dune formation and growth on a micro-tidal beach. The study area, located on a barrier island on the northern Yucatan peninsula, is characterized by persistent sea breeze conditions and a wide and shallow continental shelf. Beach topography data was collected over 5 years, on a prograding beach (0.5–7 m y-1), using a Real-Time Kinematic Differential Global Positioning System. The beach surveys (>100) cover from the subaerial beach (seaward from the existing foredune in 2015) to a 1.5 m water depth with a high spatial (0.2 m–0.5 m) and temporal (weekly to bi-weekly) resolution. Thus, shoreline evolution, offshore/onshore inner sandbar migration, and foredune initiation and growth can be analyzed from this data set. Moreover, environmental conditions, obtained from in situ observations and numerical model simulations, allow us to investigate the role of marine and aeolian processes on foredune morphodynamics. A net shoreline advance is observed, induced by the presence of a port's jetty, allowing the formation of a new foredune during the period of study. Field observations suggest that seasonal beach variability is explained by the onshore (offshore) sediment transport during fall-winter (spring-summer). The dune growth rate is well correlated with the monthly mean wind speed that reaches a maximum during spring months, when sea-breezes are more persistent and intense (W > 12 m s-1), the effective fetch length is larger, and the moisture content is lower. Empirical Orthogonal Function (EOF) analysis of beach profiles in the fast progradation area (>1 m y-1) shows that the variability is dominated by sustained beach width increase throughout the study period. On the other hand, at the slow progradation area the first and second EOF modes explain the seasonal beach and foredune variability, respectively. Thus, it is suggested that beach sand supply (during fall-winter) followed by aeolian transport (during spring) control the natural foredune growth in this region.

Offshore Wind Resource Assessment of Thailand Using Remote Sensing Technique

This paper presents the evaluation of the offshore wind resource of Thailand (the Gulf of Thailand and the Andaman Sea) using remote sensing technique. The wind speed at height of 10 m above mean sea level (A.M.S.L.) was measured using SeaWinds Scatterometer on QuickSCAT by NASA. The QuickSCAT satellite images in 1999-2009 with resolution of 1ox1o was interpreted and validated using the measured wind speed provided by the ship observation, buoy data and numerical weather prediction models. The wind speed was interpolated for 1 km resolution enhancement and extrapolated to the height of 50 m A.M.S.L.. The monthly and annual mean wind speed maps were presented in order to show the spatial and temporal variation of offshore wind energy potential. Results showed that QuickSCAT satellite images interpretation gave lower bias in the rage of wind speed of 3-18 m/s, however, the bias was high for wind speed more than 20 m/s. It found that offshore mean wind speed at height of 50 m A.M.S.L. was in the range of 3.0-6.0 m/s where the middle part of Thai offshore had the highest potential with the wind speed in the rage of 7.0-10.0 m/s. The offshore wind speed had high potential during June until August due to the influence of southwest monsoon.

Study on the characteristics of climate change in Changbai Mountain National Natural Reserve from 1958 to 2017

Climate change is an important factor affecting the ecological environment of regions, and the local climate is closely related to the ecological environment. Taking the Changbai Mountain National Natural Reserve as an example, the regional climate change was studied by analyzing the data from the years 1953–2017. It is found that the temperature of Changbai Mountain is on the rise; the maximum monthly mean temperature difference is approximately 35 °C and the annual mean temperature is 2–5 °C. The monthly mean wind speed in this area has a wavy distribution and obvious seasonal variation. The correlation analysis shows that the correlation coefficient of the monthly mean value of cumulative photosynthetically active radiation (PAR) and total solar radiation (TSR), cumulative evaporation and cumulative PAR, cumulative evaporation and TSR, temperature and cumulative PAR, and temperature and cumulative evaporation were more than 0.9, indicating a significant correlation. The annual cumulative precipitation in this area is between 600 and 1000 mm, the annual cumulative evaporation is between 1000 and 1500 mm, the annual cumulative PAR is between 6000 and 10,000 mol m−2 s−1, and the annual TSR is between 4000 and 6000 MJ m−2. The annual cumulative precipitation, annual cumulative evaporation, annual mean wind speed, annual cumulative PAR, and annual TSR show a downward trend. The climate change in this area will not have a greater negative impact on the forest, but it still needs to pay close attention to the further climate change.

다중시기 위성영상을 활용한 밀 재배지역 추출 및 생산량과 기후자료와의 상관분석

This study examined the efficiency of satellite images in terms of detecting wheat cultivation areas, and then analyzed the possibility of climate change through an correlation analysis of time series climate data from the western regions of Gyeongnam province, Korea. Furthermore, we analyzed the effect of climate change on wheat production through a multiple regression analysis with the time series wheat production and climate data. A relatively accurate distribution was achieved on the wheat cultivation area extracted through satellite image classification with an error rate of less than 10% in comparison to the statistical data. Upon correlation analysis with time series climate data, significant results were displayed in the following changes: the monthly mean temperature of the seedling stage, the monthly mean duration of sunshine, the monthly mean temperature of the growing period, the monthly mean humidity, the monthly mean temperature of the ripening stage, and the monthly mean ground temperature. Accordingly, in the study area, the monthly mean temperature, precipitation, and ground temperature generally increased whereas the monthly mean duration of sunshine and humidity decreased. The monthly mean wind speed did not display a particular change. In the multiple regression analysis results, the greatest effect on the production and productivity of wheat as climate factors included the annual mean humidity of the seedling stage, the annual mean temperature of the wintering period, and the annual mean ground temperature of the ripening stage. These results demonstrate that there is a change in wheat production depending on the climate change in the study area. in addition, it is determined that this study will be used as important basic data in the resolution of food security problems based on climate change.

Wind Energy Potential Assessment of Coastal States in South-South Nigeria Based on the Weibull Distribution Model

Energy has since become the global index for assessment of standard of living for socio-economic and industrial development. Worldwide, energy demand is rising with increasing population. Conventional energy sources such as fossil fuels are unsustainable and environmentally-unfriendly. Alternative sources of energy such as the sun, the wind etc. that are sustainable and less harmful to the environment need be exploited to meet the ever rising demand for energy, to avoid energy deficit. This paper investigated the wind energy potential of three locations in south-south Nigeria. Wind speed data measured at 10-metres height over the period 2013-2017 obtained from the Nigerian Meteorological Agency (NiMet) for Yenagoa, Calabar and Port Harcourt were evaluated using the Weibull two-parameter probability distribution model to ascertain the wind energy potential of the respective locations. The study outcome shows that during the study period, the monthly mean wind speed varied between 1.2 m/s in November and 2.3 m/s in February for Yenagoa. The same varied between 2.2 m/s in July and 3.7 m/s in February for Calabar but ranged between 1.0 m/s in July to 1.6 m/s in February for Port Harcourt. The annual mean wind speeds for Yenagoa, Calabar and Port Harcourt were 1.74 m/s, 2.85 m/s and 1.38 m/s respectively. The annual mean power densities for Yenagoa, Calabar and Port Harcourt were found to be 4.64 W/m2, 7.06 W/m2 and 3.08 W/m2 respectively while the corresponding values of the annual mean energy densities were 3.24 KW/m2, 4.93 KW/m2 and 2.14 KW/m2 respectively. The study reveal that though wind energy in the study areas is sufficient only for standalone power generating systems, water pumping and applications requiring less power, higher value of wind energy is possible if wind speed data were collected at heights above the 10m implemented in the study. Â

Prospect of Wind Energy as a Resource for Water Pumping in Ngaoundere

The modelling of wind energy conversion systems is of great importance if one intends to develop water pumping applications for a sustainable development. This study presents a technical assessment based on the measured wind data in which we investigate the possibility of coupling piston pump, roto-dynamic pump and electric pump with wind rotors for water pumping applications. Weibull distribution was used to model the monthly mean wind speed for a location in a rural area of Ngaoundere. It has been found that the Weibull distribution can be used to provide accurate estimation of the mean wind speed. The mean electric power and energy was computed based on the Vestas V25 and V100 power curves. Taking into account the wind regime characteristics of our site, we have provided the amount of water which can be expected from each type of wind pumps. From the results, it is clear that electric pump offers better performances than piston and rotodynamic pumps. Furthermore, if storage devices like batteries are combined to the system, water could be pumped at any time of the day independently of the wind availability. These systems could be very useful for people living in rural areas far from urban cities.

Water quality near Estonian harbours in the Baltic Sea as observed from entire MERIS full resolution archive

Variations and trends in water quality parameters (total suspended matter and coloured dissolved organic matter) were examined in five harbours in the eastern Baltic Sea using satellite imagery collected from 2002 to 2011 by the Medium Resolution Imaging Spectrometer (MERIS) at full spatial resolution (300×300m). In the eastern Gulf of Finland harbours (Sillamäe, Kunda) the TSM monthly variations were related to monthly mean wind speed. In Tallinn harbour, which operates >6000 vessels annually, evidence of anthropogenic impacts was identified through inter-annual TSM variations. The vessel traffic footprint was ascertained from the significant correlation (R=0.66; p=0.035) between the number of annual vessel visits and mean annual TSM concentration. In the harbour of Pärnu, located close to the river mouth, inter-annual water quality variations in terms of the CDOM concentrations were affected by the mean annual river discharge levels of the Pärnu River.

Speed distribution analysis based on maximum entropy principle and Weibull distribution function

Present study is aimed at constructing a probability distribution function using Maximum Entropy Principle and Weibull distribution function. A comparison between the two modeled function is given and error analysis is performed. For the purpose of study, 10 min averaged wind data measured at Keti Bandar, Pakistan for 21 months is used. Among many nonlinear equations as probability distribution functions with (N + 1) Lagrange multipliers and subject to moment constraints of the given set of wind speed data, a distribution function is selected for which entropy is maximized. Lagrange multipliers are determined for 5, 6, and 7 low order moments and corresponding entropies and probability distribution function are also determined. Goodness‐of‐fit is assured Kolmogorov–Smirnov (KS) test between the calculated cdfs of the theoretically function based on MaxEnt and recorded wind speed data. At 60 m, the monthly mean wind speed was varied between 5.18 and 8.15 m/s corresponding to the months of October and May with an overall mean of 6.6 m/s. On the other hand, the wind power density varied between a minimum of 82.8 W/m2 and a maximum 334 W/m2 for the months of October and May with an overall annual mean of 176.06 W/m2. Test results indicate a good fit of the modeled function based on MaxEnt as KS test statistic, Q < critical value, Q95 (∼10−2). R2, RMSE, and χ2 tests are also performed for testing goodness‐of‐fit of Weibull function. The study concluded that the MaxEnt based function is an alternative to Weibull based function. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1480–1489, 2017

Assessment of the Offshore Wind Speed Distributions at Selected Stations in the South-West Coast, Nigeria

This paper assessed the offshore wind speed distributions at 10 m height in the southwest coast of Nigeria using a high-resolution satellite observations at 0.25˚ spatial grid resolution. Satellite wind speed and direction recorded over a 10-year period (2002–2011) were derived from the CCMP L3.0 data. The monthly, seasonal and annual wind characteristics for energy conversion based on three probability distributions were assessed. The Rician and Weibull models gave better fitting of the offshore wind speed at the southwest coast compared to the Rayleigh model. Results also revealed that the monthly mean wind speed variation exhibits a rather non-monotonic trend across different grid points driven by changes in the weather system activities while further investigation reveals higher wind speed and power density at the coastal region than over land in the northern Nigeria. The monthly mean wind speed recorded at the coastal region ranges between 4.99 and 5.56 m/s, 5.32 m/s for the interannual mean wind speed with the summer (JJA) and autumn (SON) mean wind speed distributions ranging from 6.11–6.76 m/s and 4.86–6.17 m/s, respectively, at 10 m height asl. The coastal wind speed distributions show that energy conversion at the southwest region of Nigeria is viable.

Assessment of Wind Energy Potential for Small Communities in South-South Nigeria: Case Study of Koluama, Bayelsa State

Although the concept of wind energy potential assessments has matured considerably, there is only limited application and adoption in regions of energy crisis where electricity demand far exceeds supply. For Nigeria, seeking alternate sources of energy to meet its energy demand is essential and must be met in a sustainable practice. This study analyzed the electricity generation potential from wind at Koluama, Bayelsa State, Nigeria using a combination of 10-m monthly mean wind speed and direction data (1984-2013) and five year daily wind speed data (2009-2013). The data were subjected to different statistical tests and also compared with the two-parameter Weibull probability density function. Maximum mean day of year (DOY) wind speed recorded was 5.25 m/s and minimum wind speed was 0.92 m/s, while seasonal mean wind speed during the dry months (DJF) is estimated to be 4.05 m/s and 4.32 m/s during the wet months of June, July August and September (JJAS) for the 30-year period considered. Wind power density (WPD) ranged from 82 W/m2 to 145 W/m2 in November and August respectively. Lastly, small scale wind-to-electricity power generation was assessed using six (6) practical wind turbines. The AV 928 turbine had the maximum energy yield, despite relatively low capacity factor of less than 10%.

Evaluation of wind energy resource and wind turbine characteristics at two locations in China

The ten-year monthly mean wind speed data at 10, 50,100, 150 and 300 m heights over a typical year were statistically analyzed in this study to determine the potential for wind power generation at two locations (Urumqi and Xining) in China, using the two-parameters Weibull distribution function. The shape factor k and scale factor c were estimated by the maximum likelihood method. Fourteen small to medium sized commercially available wind turbines were selected for the two regions, and their mean energy outputs and capacity factors were all analyzed. Results showed that the maximum monthly mean wind speeds at different heights in Urumqi and Xining occur in November and December, respectively; while the minimum values of the two locations occur in June and July, respectively. The wind power class and potential of Urumqi and Xining are “class 1” and “poor” and “class 2” and “Marginal”, respectively. Urumqi is not suitable for large-scale electric wind-power application; however, small-scale wind turbines or wind-hybrid power systems might be a reasonable option for this site for supplying power. Xining has a better wind resource than Urumqi, and medium-scale wind turbines might be a reasonable option in Xining for supplying more electric energy. The mean energy outputs for the selected wind turbines range from 491 kWh/yr to 87,333 kWh/yr in Urumqi, while the values range from 1,071 kWh/yr to 167,237 kWh/yr in Xining. The capacity factors for these wind turbines in Urumqi and Xining range from 4.3% to 19.5% and 10.4%–27.9%, respectively. The aim of this paper is to promote the development of wind power in China by contributing to scholarly understanding of its impact on the geographical regions studied.

WIND PARAMETERS ANALYSIS ON FIVE LOCATIONS IN MACEDONIA –- THE SECOND MEASUREMENT CAMPAIGN

The world population growth and the technological development have increased the demands for electricity. Because of the limited deposits of fossil fuel and the negative impact its combustion causes to the envi ronment, the electricity production technologies are orienting towards using the renewable energy sources. From all the renewable energy sources, the most studied one over the last decade is the wind. Wind parameters of interest in electrical study are its speed and direction; once they are measured it is easy to determine other characteristic parame ters such as wind energy density on the specific location. In the manuscript, monthly and seasonal mean wind speeds are presented, alongside the wind energy density for five locations in the Republic of Macedonia, for almost four years of measurements. Key words: monthly mean wind speeds; seasonal mean wind speed; scale parameter; shape parameter; wind energy density REFERENCES: [1] Vladimir Dimcev, Vlatko Stoilkov, Krste Najdenkovski, Živko Kokolanski, Maja Celeska: Establishing wind energy data base in the Republic of Macedonia, Faculty of Electrical Engineering and IT, Skopje, September 2015. [2] Vladimir Dimcev, Vlatko Stoilkov, Krste Najdenkovski, Živko Kokolanski, Maja Celeska: Summary report for the second wind measurement campaign in the Republic of Macedonia, Faculty of Electrical Engineering and IT, Skopje, April 2016. [3] Kiril Demerdžiev: Wind energy potential measurement and data analysis, bachelor thesis, Faculty of Electrical Engineering and IT, Skopje, October 2015. [4] I. Munteanu, A. I. Bratcu, N. A. Cutululis, E. Ceanga: Optimal Control of Wind Energy Systems – Towards a Global Approach, Springer-Verlag, London, 2008. [5] A. N. Celik: A statistical Analysis of Wind Power Density Based on the Weibull and Rayleigh Models at the Southern Region of Turkey, Renewable Energy, 29, (2), 21–33 (2006). [6] Mahyoub H. Al-Buhairi, Ahmed Al-Haydari: Monthly and Seasonal Investigation of Wind Characteristics and Assessment of Wind Energy Potential in Al-Mokha, Yemen, Energy and Power Engineering, 4 (3), 125–131 (2012), doi: 10.4236/epe.2012.43017. [7] Sunday O. Oyedepo, Muyiwa S. Adaramola, Samuel S.Paul: Analysis of wind speed data and wind energy potential in three selected locations in south-east Nigeria, International Journal of Energy and Environmental Engineering, 3 (7), 1–11 (2012) (http://www. journal-ijeee.com/content/3/1/7).

Comparative Analysis of Wind Power Energy Potential at Two Coastal Locations in Bangladesh

In this study, wind conditions and its energy potential have been assessed by conducting a Weibull analysis of the wind speed data (over the period of 2002-2011) measured from a port city (Mongla) and an isolated island (Sandwip) in Bangladesh. The monthly mean wind speed at Mongla ranged from 1.60 m/s (December) to 2.47 m/s (April). The monthly values of Weibull shape parameter (k) were from 1.27 to 2.53. In addition, the values of the scale parameter (c) and the monthly wind power density ranged from 1.76 to 2.79 m/s and 3.95 to 17.45 W/ m, respectively. The seasonal mean wind speed data varied from 1.72 (fall) to 2.29 m/s (spring) with the wind power density from 5.33 (fall) to 14.26 W/ m (spring). In the case of Sandwip, the results were comparable to those of Mongla, but moderate reductions in all the comparable variables were observed. The wind data results of these two areas have been compared with those of eight other locations in the world with respect to wind power generation scale. According to this comparison, the wind power generation scale for Mongla and Sandwip was adequate for stand-alone small/micro-scale applications such as local household consumption, solar-wind hybrid irrigation pumps, and battery charging.

Potential of Wind Energy Development for Water Pumping in Ngaoundere

The modelling of wind energy conversion systems is of great importance if one intends to develop water pumping applications for a sustainable development. This paper presents a technical assessment based on the measured wind data in which we investigate the possibility of coupling piston pump, roto-dynamic pump and electric pump with wind rotors for water pumping applications. Weibull distribution is used to model the monthly mean wind speed for a location in the town of Ngaoundere. It is found that there is a good agreement between the predicted values of the mean wind speed and those obtained from data suggesting that the Weibull distribution can be used to provide accurate estimation of the mean wind speed. The mean electric power and energy are computed based on the power curves of Vestas V25 and V100. Taking into account the wind regime characteristics of our site, we provide the amount of water which can be expected from each type of wind pumps. The monthly amount of water has minimum and maximum average values of 422 m3 and 674 m3 for the piston pump, 1275 m3 and 1982 m3 for the roto-dynamic pump and 31334 m3 and 100042 m3 for the electric pump. From the results, it is clear that electric pump offer better performances than piston and rotodynamic pumps.

A Statistical Analysis and Datamining Approach for Wind Speed Predication

The wind power industry has seen an unprecedented growth in last few years. The surge in orders for wind turbines has resulted in a producers market. This market imbalance, the relative immaturity of the wind industry, and rapid developments in data processing technology have created an opportunity to improve the performance of wind farms and change misconceptions surrounding their operations. This research offers a new paradigm for the wind power industry, data-driven modeling. Each wind Mast generates extensive data for many parameters, registered as frequently as every minute. As the predictive performance approach is novel to wind industry, it is essential to establish a viable research road map. This paper proposes a data-mining-based methodology for long term wind forecasting (ANN), which is suitable to deal with large real databases. The paper includes a case study based on a real database of five years of wind speed data for a site and discusses results of wind power density was determined by using the Weibull and Rayleigh probability density functions. Wind speed predicted using wind speed data with Datamining methodology using intelligent technology as Artificial Neural Networks (ANN) and a PROLOG program designed to calculate the monthly mean wind speed.

Worldwide wind energy status and the characteristics of wind energy in Iran, case study: the province of Sistan and Baluchestan

In this paper, the recent trend of the worldwide wind energy utilisation is reviewed and the recent activities in using renewable energy sources in Iran are explained. As a case study, the wind characteristics of the province of Sistan and Baluchestan are statistically analysed. The wind characteristics such as the monthly mean wind speed and the wind power density of each station are presented. The monthly variation of the wind direction is presented and also the dominant wind direction is shown in a wind rose diagram. The values of turbulence intensity at different heights are calculated. The results show that the stations of Khash and Nosratabad are more suitable for limited off-grid utility applications. Lootak with the average annual wind power density of 388 W m−2 at the height of 40 m and constant wind direction is recommended for large-scale grid-connected wind turbines.