Research on photovoltaic ultra short-term power prediction algorithm based on attention and LSTM

Yan, Aiyun; Gu, Jin-Bo; Mu, Yahui; Li, Jingjiao; Jin, Shuowei; Wang, Aixia

doi:10.1088/1755-1315/675/1/012078

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Te LSTM cell is controlled by the following three control switches: forgetting gate ft, input gate i t , and output gate o t . Te update process of a cell is as follows [26,47]:…”

Section: Bilstm Teorymentioning

confidence: 99%

“…According to the attention mechanism, the visual system fnds the focus area in the overall image to provide additional attention while suppressing the acquisition of useless information to improve computing efciency and enhance prediction performance. In deep learning prediction, the weighted attention mechanism assigns diferent weights to data according to requirements, highlights relevant infuencing factors, and helps the model make accurate judgments [23,47]. Figure 7 provides the weighted attention mechanism.…”

Section: Attention Mechanismmentioning

confidence: 99%

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A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

Ni,

Fam,

Marsani

2024

International Journal of Intelligent Systems

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Flood disasters occur worldwide, and flood risk prediction is conducive to protecting human life and property safety. Influenced by topographic changes and rainfall, the water level fluctuates randomly and violently during the flood, introducing many noises and directly increasing the difficulty of flood prediction. A data-driven flood forecasting method is proposed based on data preprocessing and a two-layer BiLSTM-Attention network to improve forecast accuracy. First, the Variational Mode Decomposition (VMD) is used to decompose the data for reducing noise and produce suitable Intrinsic Mode Functions (IMFs); Then, an optimized two-layer attention-based Bidirectional Long Sshort-Term memory (BiLSTM-Attention) network is constructed to predict each IMF. Finally, two optimization algorithms are used to obtain the optimized parameters of VMD and BiLSTM intelligently, increasing the self-adaptability. The inertia factor of particle swarm optimization is improved and then used to optimize the five hyperparameters of BiLSTM. The proposed model reduces storage errors for smaller training sets and can achieve good performance. Three water level data sets from the Yangtze River in China are used for comparative experiments. Numerical results show that the peak height absolute error is within 2 cm, and the relative error of peak time arrival is within 30%. Compared with LSTM, BiLSTM, CNN-BiLSTM-attention, etc., the proposed model reduces the root mean square error by at least 50% and has advantages for high-risk forecasting when the water level exceeds the defense line and fluctuates prominently.

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“…Te LSTM cell is controlled by the following three control switches: forgetting gate ft, input gate i t , and output gate o t . Te update process of a cell is as follows [26,47]:…”

Section: Bilstm Teorymentioning

confidence: 99%

Section: Attention Mechanismmentioning

confidence: 99%

A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

Ni,

Fam,

Marsani

2024

International Journal of Intelligent Systems

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“…Hu employed a BP neural network for power prediction; nevertheless, it leads to underfitting because of its lack of generalization capability [14]. Yan employed the LSTM neural network to forecast power, successfully addressing the issue of gradient explosion and vanishing that occur during the training phase [15]. While according to Zhao's article, bidirectional LSTM (BiLSTM) outperforms a single LSTM structural model in terms of data feature extraction efficiency and performance.…”

Section: Introductionmentioning

confidence: 99%

Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting

Chen,

Huang

2024

EAI Endorsed Trans Energy Web

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A proposed solution is presented to address the issue of rising energy loss resulting from inaccurate power prediction in the predictive energy management strategy for composite electric power electric ship. The solution involves the development of a power prediction model that integrates Archimedes' algorithm, optimized variational modal decomposition, and BiLSTM. Within the framework of Model Predictive Control, this predictive model is utilized for power forecasting, transforming the global optimization problem into one of optimizing the power output distribution among power sources within the predictive time domain, then the optimization objective is to minimize the energy loss of the composite electric power system, and a dynamic programming algorithm is employed to solve the optimization problem within the forecast time domain. The simulation findings demonstrate a significant enhancement in the forecast accuracy of the power prediction model introduced in this study, with a 52.61% improvement compared to the AOA-BiLSTM power prediction model. Concurrently, the energy management strategy utilizing the prediction model proposed in this research shows a 1.02% reduction in energy loss compared to the prediction model control strategy based on AOA-BiLSTM, and a 15.8% reduction in energy loss compared to the ruler-based strategy.

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“…It can also effectively reduce the output limit of photovoltaic power generation systems and increase the rate of return on investment, thus increasing the economic benefits and operation management level of photovoltaic power generation systems. At present, the commonly used methods for PV power prediction include physical methods [3], statistical methods [4][5][6], meta-heuristic learning methods [7,8] and combination methods [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm

Liu

Yang

2023

Processes

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With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, forecasting accuracy and insufficient speed, etc. Using the advantages of swarm intelligence algorithms such as global optimization, strong adaptability and fast convergence, the improved Aquila optimization algorithm (AO) is used to optimize the structure of neural networks, and the optimal solution is chosen as the structure of neural networks used for subsequent prediction. However, its performance in processing sequence data with time characteristics is not good, so this paper introduces a Long Short-Term Memory (LSTM) neural network which has obvious advantages in time-series analysis. The Cauchy variational strategy is used to improve the model, and then the improved Aquila optimization algorithm (IAO) is used to optimize the parameters of the LSTM neural network to establish a model for predicting the actual photovoltaic power. The experimental results show that the proposed IAO-LSTM photovoltaic power prediction model has less error, and its overall quality and performance are significantly improved compared with the previously proposed AO-CNN model.

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Research on photovoltaic ultra short-term power prediction algorithm based on attention and LSTM

Cited by 4 publications

References 3 publications

A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting

Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm

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