Placement of weather stations in Colombia for future applications in solar and wind energy forecasting models

Author(s):  
Luis Duarte ◽  
Jesus Revollo ◽  
Daniel Betancur ◽  
Gabriel Lopez ◽  
Idi Isaac ◽  
...  
2021 ◽  
Author(s):  
Jinghui Zhang ◽  
Xiaoyu Shen ◽  
Chunhui Kong ◽  
Yagang Zhang

2009 ◽  
Vol 58 (02) ◽  
pp. 99-106 ◽  
Author(s):  
M Kay ◽  
N Cutler ◽  
A Micolich ◽  
I MacGill ◽  
H Outhred

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Farzad Arefi ◽  
Jamal Moshtagh ◽  
Mohammad Moradi

In the current work by using statistical methods and available software, the wind energy assessment of prone regions for installation of wind turbines in, Qorveh, has been investigated. Information was obtained from weather stations of Baneh, Bijar, Zarina, Saqez, Sanandaj, Qorveh, and Marivan. The monthly average and maximum of wind speed were investigated between the years 2000–2010 and the related curves were drawn. The Golobad curve (direction and percentage of dominant wind and calm wind as monthly rate) between the years 1997–2000 was analyzed and drawn with plot software. The ten-minute speed (at 10, 30, and 60 m height) and direction (at 37.5 and 10 m height) wind data were collected from weather stations of Iranian new energy organization. The wind speed distribution during one year was evaluated by using Weibull probability density function (two-parametrical), and the Weibull curve histograms were drawn by MATLAB software. According to the average wind speed of stations and technical specifications of the types of turbines, the suitable wind turbine for the station was selected. Finally, the Divandareh and Qorveh sites with favorable potential were considered for installation of wind turbines and construction of wind farms.


Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1510 ◽  
Author(s):  
Masoud Sobhani ◽  
Allison Campbell ◽  
Saurabh Sangamwar ◽  
Changlin Li ◽  
Tao Hong

Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods.


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