Improvement of Coding for Solar Radiation Forecasting in Dili Timor Leste—A WRF Case Study

Araujo, Jose Manuel Soares de

doi:10.4236/jpee.2021.92002

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…One of the most plentiful and flexible renewable energy sources, solar energy, may be harnessed in two ways: directly and indirectly. However, solar energy is the best alternative to conventional energy since it is both cheap and safe for the environment [15][16][17][18]. Renewable energy sources, such as solar and wind, are becoming increasingly popular for generating clean power because of their ability to cut down on fossil fuel consumption significantly, hence substantially lowering carbon emissions [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

et al. 2023

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Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach’s superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization.

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Section: Introductionmentioning

confidence: 99%

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

et al. 2023

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“…Solar energy is one of the most abundant and adaptable renewable energy sources; it can be used directly or indirectly. However, among all the non-conventional energy sources, solar energy is the greatest option since it is both costeffective and ecologically friendly [1][2][3][4]. The increasingly intensive use of renewable energy sources to produce clean electricity will reduce dependence on fossil fuels, allowing a strong reduction in carbon emissions [5][6][7].…”

Section: Introductionmentioning

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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“…Lonij et al [25] combined numerical weather prediction (NWP), the monitoring device of photovoltaic power station and the sky imaging technology to realize ultra-short-term photovoltaic power prediction in 10-45 min time scale. Araujo [26] adopted the shortwave coding of weather research and forecasting (WRF) to decrease error simulation. This study used high resolution based on the WRF modelling to stabilize the performance of forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of solar radiation in photovoltaic power station based on ground‐based cloud images and BP neural network

Wang

et al. 2021

IET Generation Trans & Dist

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The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra‐short‐term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground‐based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground‐based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra‐short‐term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground‐based cloud images can reach 96%, compared with the model without the feature information of ground‐based cloud images, the accuracy is improved by 5%. The proposed ultra‐short‐term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather.

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Improvement of Coding for Solar Radiation Forecasting in Dili Timor Leste—A WRF Case Study

Cited by 5 publications

References 13 publications

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

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

Forecasting of solar radiation in photovoltaic power station based on ground‐based cloud images and BP neural network

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