Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy

2014 ◽  
Vol 62 ◽  
pp. 164-176 ◽  
Author(s):  
Giovanni Gualtieri ◽  
Sauro Secci
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Southern Italy ◽  
Wind Resource

scholarly journals Wind Speed using Improved Mixture Weibull Distribution

2019 ◽  
Vol 8 (6) ◽  
pp. 1871-1874
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Weibull Distribution ◽  
Wind Farms ◽  
Resource Assessment ◽  
Estimation Of Parameters ◽  
Basic Properties ◽  
Wind Resource Assessment ◽  
Proposed Model ◽  
Wind Resource

Wind is random in nature both in space and in time. Several technologies are used in wind resource assessment (WRA).The appropriate probability distribution used to calculate the available wind speed at that particular location and the estimation of parameters is the essential part in installing wind farms. The improved mixture Weibull distribution is proposed model which is the mixture of two and three parameter Weibull distribution with parameters including scale, shape, location and weight component. The basic properties of the proposed model and estimation of parameters using various methods are discussed.

scholarly journals Time Series Modeling of Air Pollution and Its Association with Season and Climate Variables in Istanbul, Turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

scholarly journals Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5874
Author(s):  
Navid Goudarzi ◽  
Kasra Mohammadi ◽  
Alexandra St. St. Pé ◽  
Ruben Delgado ◽  
Weidong Zhu
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbines ◽  
Payback Period ◽  
Small Scale ◽  
Small Wind Turbines ◽  
Mean Wind Speed ◽  
Wind Resource ◽  
The Impact

Annual mean wind speed distribution models for power generation based on regional wind resource maps are limited by spatial and temporal resolutions. These models, in general, do not consider the impact of local terrain and atmospheric circulations. In this study, long-term five-year wind data at three sites on the North, East, and West of the Baltimore metropolitan area, Maryland, USA are statistically analyzed. The Weibull probability density function was defined based on the observatory data. Despite seasonal and spatial variability in the wind resource, the annual mean wind speed for all sites is around 3 m/s, suggesting the region is not suitable for large-scale power generation. However, it does display a wind power capacity that might allow for non-grid connected small-scale wind turbine applications. Technical and economic performance evaluations of more than 150 conventional small-scale wind turbines showed that an annual capacity factor and electricity production of 11% and 1990 kWh, respectively, are achievable. It results in a payback period of 13 years. Government incentives can improve the economic feasibility and attractiveness of investments in small wind turbines. To reduce the payback period lower than 10 years, modern/unconventional wind harvesting technologies are found to be an appealing option in this region. Key contributions of this work are (1) highlighting the need for studying the urban physics rather than just the regional wind resource maps for wind development projects in the build-environment, (2) illustrating the implementation of this approach in a real case study of Maryland, and (3) utilizing techno-economic data to determine suitable wind harnessing solutions for the studied sites.

scholarly journals Seasonal and continual wind speed modelling for the coastal urban city, Karachi

MAUSAM ◽  
2021 ◽  
Vol 69 (2) ◽  
pp. 289-296
Author(s):  
NAEEM SADIQ
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Cumulative Distribution ◽  
Maximum Deviation ◽  
Generalized Extreme Value ◽  
Urban City ◽  
Seasonal Wind

ABSTRACT.  Variation in wind speed not only indicates the strengthening or weakening of pressure systems but its role in wind farm in the vicinity of coastal area is also crucial. Probability distributions through time series of wind speed data serves foremost basic need for the said parameters. Exploratory data analysis revealed that for coastal city Karachi, maximum wind speed (~23 m/s) occurred during monsoon with its peak during postmonsoon with maximum deviation (~3.5 m/s). Mean / trimmed mean during spring and postmonsoon (~11.5 m/s) as well as in premonsoon and monsoon (~18.5 m/s) remain almost identical while minimum wind blowing during winter and postmonsoon are also identical (~6 m/s). Autumn and winter exhibits least standard deviations. Critical and statistical values have been compared for distribution modelling, while parametric values of different seasonal and continual distributions are also estimated. The study is supported by cumulative distribution functions and probability-probability plots. It is not uncommon to use Weibull distribution for wind speed modelling. By using daily data time series of wind speed for the coastal station Karachi, it has been explored that widely accepted Weibull distribution provides comparatively poor distribution results when compared to other more complicated models (i.e., Wakeby and generalized extreme value distributions]. It is found that annual and seasonal wind comes after the Wakeby distribution except premonsoon summer which follows the generalized extreme value distribution (GEV) for the city. No continual and / or seasonal wind speed follows the Weibull distribution, ultimately and / or more appropriately. The study may give some new insights for aviation and wind engineering purposes.

scholarly journals Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong

2019 ◽  
Vol 12 (1) ◽  
pp. 104 ◽  
Author(s):  
Qimin He ◽  
Kefei Zhang ◽  
Suqin Wu ◽  
Qingzhi Zhao ◽  
Xiaoming Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Hong Kong ◽  
Water Vapor ◽  
Wind Speed ◽  
Real Time ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
China Meteorological Administration ◽  
Super Typhoon

Typhoons can be serious natural disasters for the sustainability and development of society. The development of a typhoon usually involves a pre-existing weather disturbance, warm tropical oceans, and a large amount of moisture. This implies that a large variation in the atmospheric water vapor over the path of a typhoon can be used to study the characteristics of the typhoon. This is the reason that the variation in precipitable water vapor (PWV) is often used to capture the signature of a typhoon in meteorology. This study investigates the usability of real-time PWV retrieved from global navigation satellite systems (GNSS) for typhoons’ characterizations, and especially, the following aspects were investigated: (1) The correlation between PWV and atmospheric parameters including pressure, temperature, precipitation, and wind speed; (2) water vapor transportation during a typhoon period; and (3) the correlation between the movement of a typhoon and the transportation of water vapor. The case study selected for this research was Super Typhoon Mangkhut that occurred in mid-September 2018 in Hong Kong. The PWV time series were obtained from a conversion of GNSS-derived zenith total delays (ZTDs) using observations at 10 stations selected from the Hong Kong GNSS continuously operating reference stations (CORS) network, which are also located along the path of the typhoon. The Bernese GNSS Software (ver. 5.2) was used to obtain the ZTDs; and the root mean square (RMS) of the differences between the GNSS-ZTDs and International GNSS Service post-processed ZTDs time series was less than 8 mm. The RMS of the differences between the GNSS-PWVs (i.e., the ZTDs converted PWVs) and radiosonde-derived PWVs (RS-PWVs) time series was less than 2 mm. The changes in PWV reflect the variation in wind speed during the typhoon period to a certain degree, and their correlation coefficient was 0.76, meaning a significant positive correlation. In addition, a new approach was proposed to estimate the direction and speed of a typhoon’s movement using the time difference of PWV arrival at different sites. The direction and speed estimated agreed well with the ones published by the China Meteorological Administration. These results suggest that GNSS-derived PWV has a great potential for the monitoring and even prediction of typhoon events, especially for near real-time warnings.

scholarly journals Univariate Time Series Prediction of Wind speed with a case study of Yanbu, Saudi Arabia

2021 ◽  
Vol 10 (1) ◽  
pp. 257-264
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Wind Speed ◽  
Wind Energy ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Wind Speed Data ◽  
Absolute Percentage Error ◽  
Univariate Time Series

Wind energy is a promising alternativefor renewable source of energy pursued world-wide to reduce carbon emissions for a green future. The prediction of wind speed is a challenging subject and plays an instrumental role in development of wind power systems (particularly grid connected renewable energy systems where predicting wind speed facilitates manipulation of the load on the grid). Modern machine learning techniques including neural networks have been widely utilized for this purpose. Literature indicates availability of several models for estimation of the wind speed one hour ahead and the hourly wind speed data profile one day ahead. This paper considers the prediction of wind energy as a univariate time series (UVT) prediction problem and employs major prediction algorithms including the K-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Holt-Winter and ARIMA method. Forecasting a univariate time series depends only on past wind speed data values, rather than use of external data attributes like wind direction or weather forecast for prediction algorithm. In the present study (as a case-study), 13 years of hourly average wind speed data (of the period 1970-1982) of Yanbu, Saudi Arabia has been utilized to evaluate the performance of selected algorithms. Yanbu is an industrial city that plays a major role in the economy of Saudi Arabia. The findings showed that SVR, RF and ARIMA methods exhibit a better forecastingperformance in relation to four evaluation parameters of Mean Absolute Percentage Error(MAPE),Symmetric Mean Absolute Percentage Error (sMAPE),Mean Absolute Error (MAE) and Mean Absolute Scaled Error (MASE).

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