Nonlinear Modeling of Solar Radiation and Wind Speed Time Series Luigi Fortuna, Giuseppe Nunnari and Silvia Nunnari Springer, 2016, xv + 98 pages, $54.99, softcover ISBN: 978-3-319-38764-2

2018 ◽  
Vol 86 (1) ◽  
pp. 161-161
Author(s):  
Sue Ellen Haupt
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Solar Radiation ◽  
Nonlinear Modeling

scholarly journals ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2578 ◽  
Author(s):  
Neeraj Dhanraj Bokde ◽  
Zaher Mundher Yaseen ◽  
Gorm Bruun Andersen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Solar Radiation ◽  
Test Bench ◽  
Performance Metrics ◽  
R Package ◽  
Time Series Forecasting ◽  
Comparative Performance ◽  
Forecasting Methods ◽  
Natural Time

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis

2018 ◽  
Vol 6 (8) ◽  
pp. 85
Author(s):  
Naresh Patnaik ◽  
F Baliarsingh
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Time Series Analysis ◽  
Andhra Pradesh ◽  
Maximum Temperature ◽  
Climatic Parameters ◽  
Series Analysis ◽  
Annual Maximum Temperature

Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic parameters such as temperature, precipitation, solar radiation, wind speed and relative humidity by using time series analysis. The study area of research work covers the coastal districts of Odisha and some parts of Andhra Pradesh. The climatic parameters are collected over last 20 years (1993-2013) from the selected 10 stations and the prediction is made using Time Series Analysis (ARIMA Model). The annual maximum temperature, solar radiation of all districts indicates a statistically significant increase in trend, whereas in the case of wind speed and relative humidity indicates significant deceasing trend. The annual rain fall shows an increasing trend of 2.69 mm/year in all station except Srikakulam, Khordha, Jagatsinghpur and Balasore which shows a decreasing trend of 1.94, 1.29, 0.56 and 1.18 mm/year respectively. As a whole the annual maximum temperature and solar radiation shows an increase trend of 0.16 ⁰C and 0.073 MJ/m² per year respectively. Further the wind speed and relative humidity of all stations indicates a decreasing trend of 0.056 m/s and 0.003(Units in fraction) per year respectively.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

scholarly journals Atmospheric influences on infrared-laser signals used for occultation measurements between Low Earth Orbit satellites

2011 ◽  
Vol 4 (10) ◽  
pp. 2273-2292 ◽  
Author(s):  
S. Schweitzer ◽  
G. Kirchengast ◽  
V. Proschek
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Thermal Radiation ◽  
Rayleigh Scattering ◽  
Infrared Laser ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Wind Speed Profile ◽  
Upper Troposphere ◽  
Trace Species

Abstract. LEO-LEO infrared-laser occultation (LIO) is a new occultation technique between Low Earth Orbit (LEO) satellites, which applies signals in the short wave infrared spectral range (SWIR) within 2 μm to 2.5 μm. It is part of the LEO-LEO microwave and infrared-laser occultation (LMIO) method that enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and altitude levels from microwave signals and profiles of greenhouse gases and further variables such as line-of-sight wind speed from simultaneously measured LIO signals. Due to the novelty of the LMIO method, detailed knowledge of atmospheric influences on LIO signals and of their suitability for accurate trace species retrieval did not yet exist. Here we discuss these influences, assessing effects from refraction, trace species absorption, aerosol extinction and Rayleigh scattering in detail, and addressing clouds, turbulence, wind, scattered solar radiation and terrestrial thermal radiation as well. We show that the influence of refractive defocusing, foreign species absorption, aerosols and turbulence is observable, but can be rendered small to negligible by use of the differential transmission principle with a close frequency spacing of LIO absorption and reference signals within 0.5%. The influences of Rayleigh scattering and terrestrial thermal radiation are found negligible. Cloud-scattered solar radiation can be observable under bright-day conditions, but this influence can be made negligible by a close time spacing (within 5 ms) of interleaved laser-pulse and background signals. Cloud extinction loss generally blocks SWIR signals, except very thin or sub-visible cirrus clouds, which can be addressed by retrieving a cloud layering profile and exploiting it in the trace species retrieval. Wind can have a small influence on the trace species absorption, which can be made negligible by using a simultaneously retrieved or a moderately accurate background wind speed profile. We conclude that the set of SWIR channels proposed for implementing the LMIO method (Kirchengast and Schweitzer, 2011) provides adequate sensitivity to accurately retrieve eight trace species of key importance to climate and atmospheric chemistry (H2O, CO2, 13CO2, C18OO, CH4, N2O, O3, CO) in the upper troposphere/lower stratosphere region outside clouds under all atmospheric conditions. Two further species (HDO, H218O) can be retrieved in the upper troposphere.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

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