An efficient QR-BiMGM model for probabilistic PV power forecasting

Ma, Xiping; Du, Haodong; Wang, Kaiyan; Jia, Rong; Wang, Songkai

doi:10.1016/j.egyr.2022.09.077

Cited by 10 publications

(2 citation statements)

References 58 publications

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“…The model performance is optimized by employing grid search methods, which systematically explore various combinations of parameters. In this study, a cross-validation-based grid search approach was employed to select the bandwidth parameter relevant to the research [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Guo,

Xu,

Wang

et al. 2024

Sensors

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Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

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Section: Methodsmentioning

confidence: 99%

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Guo,

Xu,

Wang

et al. 2024

Sensors

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“…Optimal operation and the size of the storage facilities based on a probabilistic PVF were investigated in (Besson et al, 2021). Moreover, a probabilistic PV power forecasting was proposed in (Ma et al, 2022). It relied on Bi-Minimal Gated Memory (MGM) in order to improve the forecast accuracy and training using a bi-directional propagation model.…”

Section: Literature Reviewmentioning

confidence: 99%

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

Bâra,

Oprea

2024

Journal of Forecasting

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The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10.

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A Probabilistic Density Prediction Method for Power Plant Production Data Based on QR-GRU

Zheng,

Zhu,

Zhou

2024

Lecture Notes in Electrical Engineering

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An efficient QR-BiMGM model for probabilistic PV power forecasting

Cited by 10 publications

References 58 publications

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

A Probabilistic Density Prediction Method for Power Plant Production Data Based on QR-GRU

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