Calibration and evaluation of sunshine-based empirical models for estimating daily solar radiation in Iran

Naserpour, Somayeh; Zolfaghari, Hasan; Firouzabadi, Parviz Zeaiean

doi:10.1016/j.seta.2020.100855

Cited by 16 publications

(7 citation statements)

References 58 publications

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“… [ 35 ] Iran Twenty-one sunshine-based empirical models Number of days and sunshine Daily solar radiation 2007–2017 R 2 , MAE, MBE, RRMSE, MAPE, RMSE, and MBE 21 different sunshine-based empirical models were compared and calibrated. [ 36 ] Adrar, Algeria Proposed 6 temperature-based models Daily air temperature Daily average horizontal global solar radiation 4 years period MBE, RMSE, and R 2 Proposed M4 model shows best performance (R 2 = 0.87). [ 37 ] Industrial City, the Kingdom of Saudi Arabia Linear, Quadratic and Logarithmic Relative humidity, ambient temperature, and sunshine duration Global horizontal radiation January–December 2016 r, R 2 , RMSE, MBE, MABE, and MAPE 3 different models were examined and compared, the quadratic model has the highest r and R 2 .…”

Section: Literature Reviewmentioning

confidence: 99%

A state of art review on estimation of solar radiation with various models

Gürel¹,

Ağbulut²,

Bakır³

et al. 2023

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

A state of art review on estimation of solar radiation with various models

Gürel¹,

Ağbulut²,

Bakır³

et al. 2023

Heliyon

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“…Empirical methods for R s estimation have been widely established based on different input variables considering the low data requirements and computation. These statistical models are divided into sunshine‐ (Ångström, 1924; Naserpour et al, 2020), cloud‐ (Badescu & Dumitrescu, 2015; Ehnberg & Bollen, 2005), and temperature‐based models (Feng et al, 2019; Yacef et al, 2014). Studies have shown that sunshine–based models outperformed other models (Abdul‐Aziz et al, 1993; De, Souza, et al, 2016; Trnka et al, 2005), especially the Ångström–Prescott (A‐P) method (Prescott, 1940).…”

Section: Introductionmentioning

confidence: 99%

“…established based on different input variables considering the low data requirements and computation. These statistical models are divided into sunshine- (Ångström, 1924;Naserpour et al, 2020), cloud- (Badescu & Dumitrescu, 2015;Ehnberg & Bollen, 2005), and temperature-based models (Feng et al, 2019;Yacef et al, 2014).…”

mentioning

confidence: 99%

Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Chen,

Feng,

et al. 2024

Hydrological Processes

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Accurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements.

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“…The empirical prediction models can be found in the literature and are often used to estimate monthly solar irradiance. The most widely used approach is a sunshine-based predictive model [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data

Alzahrani

2022

Sensors

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Concerns over fossil fuels and depletable energy sources have motivated renewable energy sources utilization, such as solar photovoltaic (PV) power. Utilities have started penetrating the existing primary grid with renewable energy sources. However, penetrating the grid with photovoltaic energy sources degrades the stability of the whole system because photovoltaic power depends on solar irradiance, which is highly intermittent. This paper proposes a prediction method for non-stationary solar irradiance. The proposed method uses an adaptive extreme learning machine. The extreme learning machine method uses approximated sigmoid and hyper-tangent functions to ensure faster computational time and more straightforward microcontroller implementation. The proposed method is analyzed using the hourly weather data from a specific site at Najran University. The data are preprocessed, trained, tested, and validated. Several evaluation metrics, such as the root mean square error, mean square error, and mean absolute error, are used to evaluate and compare the proposed method with other recently introduced approaches. The results show that the proposed method can be used to predict solar irradiance with high accuracy, as the mean square error is 0.1727. The proposed approach is implemented using a solar irradiance sensor made of a PV cell, a temperature sensor, and a low-cost microcontroller.

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Calibration and evaluation of sunshine-based empirical models for estimating daily solar radiation in Iran

Cited by 16 publications

References 58 publications

A state of art review on estimation of solar radiation with various models

A state of art review on estimation of solar radiation with various models

Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data

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