Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm

Beyaztaş, Ufuk; Salih, Sinan Q.; Chau, Kwok Wing; Al‐Ansari, Nadhir; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1080/19942060.2019.1676314

Cited by 23 publications

(14 citation statements)

References 42 publications

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“…The apparent concern is the nature of the time series and selection of appropriate methods such as the complete variation from seasonality, trends, and random parameters, that should be handled with each method. For instance, one forecasting model may perform better for a seasonal time series but show poor accuracy as compared to other models for a trendy or a random one [22]. Besides, interpretations might be affected by the error metric selection, for example, Root mean square error (RMSE), as different metrics have different objectives [23].…”

Section: Introductionmentioning

confidence: 99%

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

Bokde

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Andersen

2020

Energies

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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.

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Section: Introductionmentioning

confidence: 99%

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

Bokde

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Andersen

2020

Energies

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“…CORDEX outputs are being increasingly used for climate change impact analysis in regional studies because of their higher resolutions (Dosio, 2016;Koenigk, Berg, & Döscher, 2015;Mariotti, Diallo, Coppola, & Giorgi, 2014). Machine learning models, such as extreme learning machine, artificial neural network and neuro-fuzzy systems, have been successfully used for wind speed forecasting (Panapakidis, Michailides, & Angelides, 2019;Yang & Chen, 2019), river flow and flood management (Cheng, Lin, Sun, & Chau, 2005;Fotovatikhah et al, 2018;Yaseen, Sulaiman, Deo, & Chau, 2019), predicting solar radiation (Beyaztas, Salih, Chau, Al-Ansari, & Yaseen, 2019), estimation of evaporation (Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018;Qasem et al, 2019;Salih et al, 2019) and wind power estimation and extraction, among their many other applications (Petković et al, 2014;Shamshirband et al, 2016). Khanali, Ahmadzadegan, Omid, Nasab, and Chau (2018) used a genetic algorithm to obtain an optimized layout for wind farm turbines in Iran.…”

Section: Introductionmentioning

confidence: 99%

Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system

Nabipour

Mosavi

Hajnal

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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Climate change impacts and adaptations are ongoing issues that are attracting the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken into consideration. An adaptive neuro-fuzzy inference system (ANFIS)-based post-processing technique was used to match the power outputs of the regional climate model (RCM) with those obtained from reference data. The near-surface wind data obtained from an RCM were used to investigate climate change impacts on the wind power resources in the Caspian Sea. After converting nearsurface wind speed to turbine hub-height speed and computation of wind power density, the results were investigated to reveal mean annual power, seasonal and monthly variability for 20 year historical (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) and future (2081-2100) periods. The results revealed that climate change does not notably affect the wind climate over the study area. However, a small decrease was projected in the future simulation, revealing a slight decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution, with winter and summer having the highest values.The results indicate that the middle and northern parts of the Caspian Sea have the highest values of wind power. However, the results of the post-processing technique using the ANFIS model showed that the real potential of wind power in the area is lower than that projected in the RCM. ARTICLE HISTORY

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“…Solar energy is modern, pure, renewable, and inexhaustible (Ahmadi et al, 2020;Beyaztas et al, 2019;Samadianfard et al, 2019). Transforming solar energy into thermal energy via solar collectors is a topic of interest to many researchers (Abuşka, 2018;Kabeel et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Numerical calculations of the thermal-aerodynamic characteristics in a solar duct with multiple V-baffles

Menni

Ghazvini

Ameur

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The study aimed to enhance the heat transport by improving the hydrodynamic structure of the system by changing and restructuring the duct's internal geometry. Modern fins, of the shape 'V', have been proposed with different dimensions, and they are periodically arranged over the duct surfaces. The most important steps of this research are the change in the V-fin attack-angle (40°-80°), length (H b /2, 3H b /4, H b , 5H b /4 and 3H b /2), and separation length (D s /2, 3D s /4, D s and 5D s /4), as well as the flow rate (6 × 10 3-3 × 10 4). The study yielded an optimum case for a 40-degree attack-angle, with a factor of thermal enhancement of 2.163 for the highest value of Reynolds number. On the other hand, improving the length of the V-fins or decreasing in the space between them, increases the flow strength by enlarging the recycling cells, which reflects on the hydrodynamic behavior, and changes the heat transfer. The presence of this new model of fins also highlights a hydrothermal improvement ranging between 1.196 and 23.779 percent compared to the previously indicated models, reflecting the effectiveness of the new system of solar heat exchangers with air V-finned ducts.

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Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm

Cited by 23 publications

References 42 publications

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

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

Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system

Numerical calculations of the thermal-aerodynamic characteristics in a solar duct with multiple V-baffles

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