Solar Radiation Forecasting based on Neural Network in Guangzhou

Huang, Chiou-Jye; Ma, Yuxuan; Chen, Yung‐Hsiang

doi:10.1109/cacs50047.2020.9289830

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…They identified the most important variables by analyzing eight weather parameters using the Pearson correlation coefficient. Meanwhile, in the study of solar radiation prediction, Huang et al [13] used historical information from a continuous time series obtained by PPMCC to analyze the relationship between the sun radiation intensity and six other meteorological parameters. In another study, Ojo [14] considered air temperature, solar radiation, and wind speed data from the National Aeronautics and Space Administration and evaluated ten existing models.…”

Section: Figure 1 Solar Irradiance Profile In Malaysiamentioning

confidence: 99%

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Tan,

N. Afrouzi,

Ahmed

et al. 2024

fusus

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Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change, and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement them in different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle, correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.

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Section: Figure 1 Solar Irradiance Profile In Malaysiamentioning

confidence: 99%

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Tan,

N. Afrouzi,

Ahmed

et al. 2024

fusus

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show abstract

“…Different machine learning approaches were used; the multi-layer perceptron (MLP) algorithm gave the best outcome with an MSE equal to 0.2222. In [28], the backpropagation (BP) neural network is used to construct an effective forecasting model of solar radiation. As inputs to the developed model, the authors used weather parameters, specifically the rainfall, air humidity, and clear-air index; the obtained RMSE equals 0.4708.…”

Section: Related Workmentioning

confidence: 99%

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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Estimation for Solar Radiation on Different Places from Adjacent Weather Stations Data Through Mathematical Regression Model

Garcia-Hernandez,

Rodriguez-Armenta,

Garcia-Garcia

2023

2023 15th IEEE International Conference on Industry Applications (INDUSCON)

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Solar Radiation Forecasting based on Neural Network in Guangzhou

Cited by 3 publications

References 11 publications

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Estimation for Solar Radiation on Different Places from Adjacent Weather Stations Data Through Mathematical Regression Model

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