A combination of novel hybrid deep learning model and quantile regression for short‐term deterministic and probabilistic PV maximum power forecasting

Tuyen, Nguyen Duc; Thanh, Nguyen Huu; Huu, Vu Xuan Son; Fujita, Goro

doi:10.1049/rpg2.12634

Cited by 13 publications

(2 citation statements)

References 63 publications

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“…Unlike traditional regression techniques, which rely on assumptions about the error distribution, QR directly models the error distribution function. Therefore, it does not impose any restrictive assumptions regarding datasets or prediction error normality [ 34 ]. The corresponding formula for QR is as follows:

where

is the conditional quantile of the dependent variable, where the value of

ranges from 0 to 1.…”

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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where

is the conditional quantile of the dependent variable, where the value of

ranges from 0 to 1.…”

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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show abstract

“…These data-driven models are built on historical PV power data and numerical weather prediction. The hybrid model [8,[14][15][16][17] is to combine different models and choose a suitable way to combine the advantages of different models and obtain higher prediction accuracy. However, the computation of these models can become complex.…”

Section: Introductionmentioning

confidence: 99%

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Zhang,

Hong,

Ibrahim

et al. 2023

IET Renewable Power Gen

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Distributed photovoltaic (PV) systems often lack adequate measurements due to cost considerations, which makes it very difficult to predict them accurately. Here, an approach is proposed for behind‐the‐meter (BTM) PV power prediction using attention‐LSTM neural network and transfer learning. First, the weather is classified into four types based on the deviation ratio β. Second, the correlation analysis algorithm identifies the weather factors that contribute the most to PV power generation as GHI, DNI, humidity, and temperature. Then, attention‐LSTM pre‐trained model is constructed, in which the LSTM network fully extracts the temporal characteristics of PV power generation, while the attention mechanism enhances the attention to the important information in the input. Finally, a novel BTM PV short‐term power prediction method using transfer learning that the upper layer parameters of the pre‐trained model were frozen, and a small amount of DP‐NB data was used to fine‐tune the model. The proposed methodology has higher prediction accuracy compared with other benchmark methods under four weather types, and the time cost is saved by freezing the parameters of the first layer of the network by 37.2%.

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Short-term PV power forecast using hybrid deep learning model and Variational Mode Decomposition

et al. 2023

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A combination of novel hybrid deep learning model and quantile regression for short‐term deterministic and probabilistic PV maximum power forecasting

Cited by 13 publications

References 63 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

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Short-term PV power forecast using hybrid deep learning model and Variational Mode Decomposition

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