scholarly journals WRF Wind Speed Simulation and SAM Wind Energy Estimation: A Case Study in Dili Timor Leste

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 35382-35393 ◽  
Author(s):  
Jose Manuel Soares De Araujo
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Estimation ◽  
Timor Leste ◽  
Speed Simulation

Wind Potential Assessment for the Utilization from Wind Energy in the Household Level: A Case Study of Nakhonratchasima Thailand

2016 ◽  
Vol 835 ◽  
pp. 749-752
Author(s):  
Yuttachai Keawsuntia
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Alternative Energy ◽  
Energy Resource ◽  
Energy Utilization ◽  
Distribution Method ◽  
Household Level ◽  
Wind Speed Data ◽  
Wind Potential

Wind energy is an important alternative energy resource because of it clean, does not cause pollution and it can be used as replacement of a fossil fuel energy. Utilization of the wind energy, the wind speed data has to be analyzed to make sure before use it. In this article is to present the wind speed data analysis by using Weibull distribution method. Wind speed data from the meteorological station at Pakchong district, Nakhonratchasima province, Thailand was used as the case study. The results show that this area has wind speed about 2.5 to 3.5 m/s. The average wind power density was 17.513 W/m2 and the total wind energy was 153.9819 kW·hr/m2 per year. This wind potential of this area can be used for water pumping and electricity generating for use in a household.

Characterizing Wind Speed and Air Density for Wind Energy Estimation

2009 ◽  
Author(s):  
Xiuli Qu ◽  
Jing Shi
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Density Distribution ◽  
North Dakota ◽  
Distribution Model ◽  
Energy Potential ◽  
Energy Estimation ◽  
Air Density ◽  
Wind Energy Potential

Wind energy is the fastest growing renewable energy source in the past decade. To estimate the wind energy potential for a specific site, the long-term wind data need to be analyzed and accurately modeled. Wind speed and air density are the two key parameters for wind energy potential calculation, and their characteristics determine the long-term wind energy estimation. In this paper, we analyze the wind speed and air density data obtained from two observation sites in North Dakota and Colorado, and the variations of wind speed and air density in long term are demonstrated. We obtain univariate statistical distributions for the two parameters respectively. Excellent fitting performance can be achieved for wind speed for both sites using conventional univariate probability distribution functions, but fitting air density distribution for the North Dakota site appears to be less accurate. Furthermore, we adopt Farlie-Gumbel-Morgenstern approach to construct joint bivariate distributions to describe wind speed and air density simultaneously. Overall, satisfactory goodness-of-fit values are achieved with the joint distribution models, but the fitting performance is slightly worse compared with the univariate distributions. Further research is needed to improve air density distribution model and the joint bivariate distribution model for wind speed and air density.

A methodology for improving wind energy production in low wind speed regions, with a case study application in Iraq

2019 ◽  
Vol 127 ◽  
pp. 89-102 ◽  
Author(s):  
Abdul Salam Darwish ◽  
Sabry Shaaban ◽  
Erika Marsillac ◽  
Nazar Muneam Mahmood
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Production ◽  
Low Wind Speed ◽  
Study Application

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).

scholarly journals Integrated Forecasting Method for Wind Energy Management: A Case Study in China

Processes ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 35 ◽  
Author(s):  
Yao Dong ◽  
Lifang Zhang ◽  
Zhenkun Liu ◽  
Jianzhou Wang
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Farms ◽  
Wind Speed Forecasting ◽  
Electricity System ◽  
Generation Scheduling ◽  
Forecasting Performance ◽  
Data Pretreatment ◽  
Combined Strategy

Wind speed forecasting helps to increase the efficacy of wind farms and prompts the comparative superiority of wind energy in the global electricity system. Many wind speed forecasting theories have been widely applied to forecast wind speed, which is nonlinear, and unstable. Current forecasting strategies can be applied to various wind speed time series. However, some models neglect the prerequisite of data preprocessing and the objective of simultaneously optimizing accuracy and stability, which results in poor forecast. In this research, we developed a combined wind speed forecasting strategy that includes several components: data pretreatment, optimization, forecasting, and assessment. The developed system remedies some deficiencies in traditional single models and markedly enhances wind speed forecasting performance. To evaluate the performance of this combined strategy, 10-min wind speed sequences gathered from large wind farms in Shandong province in China were adopted as a case study. The simulation results show that the forecasting ability of our proposed combined strategy surpasses the other selected comparable models to some extent. Thus, the model can provide reliable support for wind power generation scheduling.

Autonomous energy supply based on the wind-energy complex and hydrogen energy storage

2019 ◽  
pp. 12-26
Author(s):  
S. I. Nefedkin ◽  
A. O. Barsukov ◽  
M. I. Mozgova ◽  
M. S. Shichkov ◽  
M. A. Klimova
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Power Supply ◽  
Energy Supply ◽  
Heat Supply ◽  
Hot Water ◽  
Successful Implementation ◽  
High Wind ◽  
Wind Generators ◽  
Weak Wind

The paper proposes an alternative scheme of guaranteed electricity and heat supply of an energy-insulated facility with a high potential of wind energy without the use of imported or local fuel. The scheme represents a wind power complex containing the park of wind generators located at the points with high wind potential. The wind generators provide guaranteed power supply even in periods of weak wind. For heat supply of the consumer, all surplus of the electric power goes on thermoelectric heating of water in tanks of accumulators, and also on receiving hydrogen by a method of electrolysis of water. The current heat supply is carried out with the use of hot water storage tanks, and the heat supply during the heat shortage is carried out by burning the stored hydrogen in condensing hydrogen boilers. We have developed the algorithm of calculation and the program "Wind in energy" which allows calculating annual balance of energy and picking up necessary quantity of the equipment for implementation of the scheme proceeding from the annual schedule of thermal and electric loading, and also potential of wind energy in the chosen region. The calculation-substantiation of the scheme proposed in relation to the real energy-insulated object Ust-Kamchatsk (Kamchatka) is carried out. The equipment for the implementation of an alternative energy supply scheme without the use of imported fuel is selected and compared with the traditional energy supply scheme based on a diesel power plant and a boiler house operating on imported fuel. With the introduction of an alternative power supply scheme, the equipment of the traditional scheme that has exhausted its resource can be used for backup power supply. Using climate databases, a number of energy-insulated facilities in the North and East of Russia with high wind energy potential are considered and the conditions for the successful implementation of the energy supply scheme are analyzed. This requires not only a high average annual wind speed, but also a minimum number of days of weak wind. In addition, it is necessary that the profile of the wind speed distribution in the annual section coincides with the profile of the heat load consumption.

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