Short-term Solar Power Forecasting Using XGBoost with Numerical Weather Prediction

Phan, Quoc-Thang; Wu, Yuan‐Kang; Dzung, Phan Quoc

doi:10.1109/ifeec53238.2021.9661874

Cited by 17 publications

(7 citation statements)

References 8 publications

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“…However, we could judge that PV power to predict would not be located out of the boundary set. In addition, in many cases, XGBoost performs excellently predicting various power data [30,31]. Therefore, we choose XGboost as one of the models to predict PV generation.…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

Heo,

Kim,

Choi

2023

Sensors

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This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation.

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Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

Heo,

Kim,

Choi

2023

Sensors

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“…With the continuous development of image feature extraction technology, the failure detection method based on thermal infrared images has received widespread attention [12]. Phan et al [13] propose a two-stage neural network single-step solar power prediction method that is applicable to small samples, solving the difficulty of applying conventional short-term solar power prediction methods. Manoharan et al [14] present a simple and enhanced perturb and observation method, which is enhanced by including the change in current, in addition to the changes in output voltage and output power of the PV module.…”

Section: Related Workmentioning

confidence: 99%

Infrared Fault Classification Based on the Siamese Network

Zhang,

Wang,

Bao

et al. 2023

Applied Sciences

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The rapid development of solar energy technology has led to significant progress in recent years, but the daily maintenance of solar panels faces significant challenges. The diagnosis of solar panel failures by infrared detection devices can improve the efficiency of maintenance personnel. Currently, due to the scarcity of infrared solar panel failure samples and the problem of unclear image effective features, traditional deep neural network models can easily encounter overfitting and poor generalization performance under small sample conditions. To address these problems, this paper proposes a solar panel failure diagnosis method based on an improved Siamese network. Firstly, two types of solar panel samples of the same category are constructed. Secondly, the images of the samples are input into the feature model combining convolution, adaptive coordinate attention (ACA), and the feature fusion module (FFM) to extract features, learning the similarities between different types of solar panel samples. Finally, the trained model is used to determine the similarity of the input solar image, obtaining the failure diagnosis results. In this case, adaptive coordinate attention can effectively obtain interested effective feature information, and the feature fusion module can integrate the different effective information obtained, further enriching the feature information. The ACA-FFM Siamese network method can alleviate the problem of insufficient sample quantity and effectively improve the classification accuracy, achieving a classification accuracy rate of 83.9% on an open-accessed infrared failure dataset with high similarity.

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“…The XGBoost algorithm's effectiveness in forecasting solar PV production was also explored in Refs. [ 16 , 17 ] for day-ahead and hour-ahead scenarios. Their studies observed significant improvement in the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

Asiedu,

Nyarko,

Boahen

et al. 2024

Heliyon

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Short-term Solar Power Forecasting Using XGBoost with Numerical Weather Prediction

Cited by 17 publications

References 8 publications

Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

Infrared Fault Classification Based on the Siamese Network

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

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