The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.
Diffuse irradiance is a fundamental component in solar resource considerations. Diffuse irradiance can be accurately determined by calculation from global and beam normal (direct) measurements. However, beam solar measurements can be expensive and therefore shadow bands are often used along with pyranometers to mask the solar disk. The errors that result from the use shadow bands are well known and have been studied by numerous authors. The thrust of this article is to examine four recognized correction techniques for correcting shadow band based diffuse irradiance and statistically evaluate their individual performances using data collected in Almerı´a, Spain. Almerı´a is located in southern Spain and has a healthy solar irradiation budget.
There has been an enormous increase in the use of solar heating and photovoltaic systems worldwide. Building professionals to better design buildings, windows and other solar related constructs require current and accurate compilations of solar irradiance data. Frequently, enough care is not exercised with respect to quality of the measurements. Traditionally, researchers have used several methods for the quality assessment of solar irradiance data. This article addresses two of those methods, visual and quartile analysis, often used to identify “outliers” in the large datasets typically found in solar energy related research. The present work introduces another method for identification of erroneous data, based on standard deviations, using the clearness index and the diffuse ratio. This method is highly efficient in terms of its algorithmic approach.
The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.
The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.
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