Deep attention ConvLSTM-based adaptive fusion of clear-sky physical prior knowledge and multivariable historical information for probabilistic prediction of photovoltaic power

Bai, Mingliang; Chen, Yunxiao; Zhao, Xinyu; Liu, Jinfu; Yu, Daren

doi:10.1016/j.eswa.2022.117335

Cited by 22 publications

(4 citation statements)

References 60 publications

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“…They have been applied to PV power forecasting by individual scholars in the latest research. Research work [33] combines an AM with ConvLSTM to utilize the AM to adjust the weights of physical a priori features and historical PV data in the input data. The experimental results show that the proposed method significantly improves the accuracy of annual PV generation forecasting.…”

Section: Related Workmentioning

confidence: 99%

“…These studies mainly focus on investigating the methods of combining CNNs with other models, with the aim of combining the respective advantages of multiple models to obtain better prediction performance than using a single model. However, this approach of combining other models exploits the advantages of each model while mixing in their respective disadvantages (e.g., LSTM is limited in its ability to extract spatial features [33]). How to design architectures that maximize the advantages of the models based on the actual scenario's requirements can be further investigated.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

Ren,

Zhang,

Yan

et al. 2024

Sustainability

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Accurate photovoltaic (PV) power forecasting allows for better integration and management of renewable energy sources, which can help to reduce our dependence on finite fossil fuels, drive energy transitions and climate change mitigation, and thus promote the sustainable development of renewable energy sources. A convolutional neural network (CNN) forecasting method with a two-input, two-scale parallel cascade structure is proposed for ultra-short-term PV power forecasting tasks. The dual-input pattern of the model is constructed by integrating the weather variables and the historical power so as to convey finer information about the interaction between the weather variables and the PV power to the model; the design of the two-branch, two-scale CNN model architecture realizes in-depth fusion of the PV system data with the CNN’s feature extraction mechanism. Each branch introduces an attention mechanism (AM) that focuses on the degree of influence between elements within the historical power sequence and the degree of influence of each meteorological variable on the historical power sequence, respectively. Actual operational data from three PV plants under different meteorological conditions are used. Compared with the baseline model, the proposed model shows a better forecasting performance, which provides a new idea for deep-learning-based PV power forecasting techniques, as well as important technical support for a high percentage of PV energy to be connected to the grid, thus promoting the sustainable development of renewable energy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

Ren,

Zhang,

Yan

et al. 2024

Sustainability

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show abstract

“…The fireworks algorithm (FWA) was introduced to search for the optimal values of the hyperparameters of the network model with high prediction accuracy. Bai et al (2022) proposed a new method for short-term PV power prediction using deep attention convolutional long and short-term memory (Conv-LSTM) networks and kernel density estimation. The proposed method can achieve the optimal fusion of historical data and clear sky prior knowledge, and significantly improve the accuracy of PV power prediction in all seasons of a year.…”

Section: Introductionmentioning

confidence: 99%

Short-term photovoltaic power prediction based on fractional Levy stable motion

Zheng,

Song,

Cheng

et al. 2023

Energy Exploration & Exploitation

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Accurate prediction of photovoltaic (PV) power generation is the key to daily dispatch management and safe and stable grid operation. In order to improve the accuracy of the prediction, a finite iterative PV power prediction model with long range dependence (LRD) characteristics was developed using fractional Lévy stable motion (fLsm) and applied to a real dataset collected in the DKASC photovoltaic system in Alice Springs, Australia. The LRD prediction model considers the influence of current and past trends in the stochastic series on the future trends. Firstly, the calculation of the maximum steps prediction was introduced based on the maximum Lyapunov. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the order stochastic differential equation (FSDE) which describes the fLsm can be obtained. The parameters of the FSDE were estimated by using a novel characteristic function method. The PV power forecasting model with the LRD characteristics was obtained by discretization of FSDE. By comparing statistical performance indicators such as root max error, mean square error with Conv-LSTM, BiLSTM, and GA-LSTM models, the performance of the proposed fLsm model has been demonstrated. The proposed methods can provide better theoretical support for the stable and safe operation of PV grid connection. They have high reference value for grid dispatching department.

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“…Scholars at home and abroad often turn to non-parametric estimation methods to deal with the above problems. Kernel density estimation (KDE), as a type of non-parametric estimation, has been extensively applied in power generation prediction [20][21][22][23]. KDE can effectively deal with the uncertainty of the array output and provide a new direction for PV array performance evaluation.…”

Section: Introductionmentioning

confidence: 99%

Classification Method of Photovoltaic Array Operating State Based on Nonparametric Estimation and 3σ Method

Tong¹,

Li²,

Ren

et al. 2023

Sustainability

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Photovoltaic (PV) array, as the key component of large-scale PV power stations, is prone to frequent failure that directly affects the efficiency of PV power stations. Therefore, accurate classification of the operating state of PV arrays is the basis for fault location. Thus, a novel classification method for PV array operating state was designed based on nonparametric estimation and a 3σ method. The actual data analysis proves the hypothesis that performance ratio (PR) distribution characteristics of PV arrays can characterize the operating state of PV arrays. The modeling curve of the PV array with an excellent performance has only one peak and the peak value is large, while the distribution curve of the PV array with a poor performance has a small peak. In this paper, the distribution characteristics of PV arrays are modeled, the peak value is used to classify the operating state of PV arrays, and finally the effectiveness of the proposed method is compared. Overall, this paper makes a valuable contribution by proposing a novel method for accurately classifying the operating state of PV arrays. The proposed method can help improve the efficiency and fault diagnosis of PV power stations.

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Deep attention ConvLSTM-based adaptive fusion of clear-sky physical prior knowledge and multivariable historical information for probabilistic prediction of photovoltaic power

Cited by 22 publications

References 60 publications

A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

Short-term photovoltaic power prediction based on fractional Levy stable motion

Classification Method of Photovoltaic Array Operating State Based on Nonparametric Estimation and 3σ Method

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