Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism

Chen, Wengang; He, Hongying; Liu, Jianguo; Yang, Jian; Zhang, Ke; Luo, Diansheng

doi:10.3389/fenrg.2023.1123558

Cited by 2 publications

(1 citation statement)

References 22 publications

(19 reference statements)

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“…The agent of Q-VMD-RLG continuously learns to find the optimal policy π based on action value function Q(S(t), a(t)) and continuously updates action a(t) to maximize the value of Q [72][73][74][75][76], thus obtaining the optimal action-value function Q*(S(t), a(t)) [77,78], which is updated as shown in Equation (16).…”

Section: Markov Dynamic Decision Process Of Q-vmd-rlg Modelmentioning

confidence: 99%

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

He,

Zhao,

Zhang

et al. 2024

The Journal of Engineering

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Short‐term load forecasting is critical for power system planning and operations, and ensemble forecasting methods for electricity loads have been shown to be effective in obtaining accurate forecasts. However, the weights in ensemble prediction models are usually preset based on the overall performance after training, which prevents the model from adapting in the face of different scenarios, limiting the improvement of prediction performance. In order to improve the accurateness and validity of the ensemble prediction method further, this paper proposes an ensemble deep reinforcement learning approach using Q‐learning dynamic weight assignment to consider local behaviours caused by changes in the external environment. Firstly, the variational mode decomposition is used to reduce the non‐stationarity of the original data by decomposing the load sequence. Then, the recurrent neural network (RNN), long short‐term memory (LSTM), and gated recurrent unit (GRU) are selected as the basic power load predictors. Finally, the optimal weights are ensembled for the three sub‐predictors by the optimal weights generated using the Q‐learning algorithm, and the final results are obtained by combining their respective predictions. The results show that the forecasting capability of the proposed method outperforms all sub‐models and several baseline ensemble forecasting methods.

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Section: Markov Dynamic Decision Process Of Q-vmd-rlg Modelmentioning

confidence: 99%

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

He,

Zhao,

Zhang

et al. 2024

The Journal of Engineering

View full text Add to dashboard Cite

show abstract

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

Zhang,

Lei,

et al. 2024

Front. Energy Res.

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The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

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Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism

Cited by 2 publications

References 22 publications

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

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