Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Gao, Mingming; Li, Jianjing; Hong, Feng; Long, Dongteng

doi:10.3390/app9153192

Cited by 32 publications

(11 citation statements)

References 28 publications

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“…In addition, when comparing LSTM, many references analyzed the prediction results in different seasons. 34,45,46 The prediction accuracy is approximately 90% to 93%, which is similar to the prediction results in this article. Although some optimizations for LSTM will increase its prediction accuracy, as time increases, the prediction accuracy of LSTM will significantly decrease.…”

Section: Discussionsupporting

confidence: 88%

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Ultra‐short‐term photovoltaic power forecasting of multifeature based on hybrid deep learning

Huang

Zhou

Yang

2021

Intl J of Energy Research

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Summary The output power of photovoltaic power has randomness and volatility, which poses new challenges to the peak shaving and dispatching of the power system. Therefore, the accurate prediction of photovoltaic power output is an effective way to maintain the security and stability of the power grid. In this article, a hybrid prediction model based on improved convolutional neural network and bidirectional gated recurrent unit is proposed to predict PV output power. Firstly, the concept of time relevance is introduced to fuse this feature with weather characteristics as the input of the prediction model. Secondly, the time series generated adversarial neural network is applied to the field of photovoltaic power generation prediction for the first time to enhance the dataset obtained. Thirdly, the model proposed in this article is trained and compared with other models under different characteristics, different weather conditions, and different forecast time steps. The experimental results show that the hybrid deep learning model proposed in this article has better prediction performance. Under different characteristics, the model proposed in this article has the highest prediction accuracy. After data enhancement, the prediction accuracy of each model has been improved by 1% to 3%. Among them, the prediction accuracy of the model proposed in this article is 0.977 and 0.960, respectively. Under different weather conditions and different time steps, the proposed model has the best stability. Under cloudy and rainy weather, the prediction accuracy of the proposed model only decreases by about 6%. In addition, the prediction accuracy of the proposed model only decreases by approximately 1.5% to 4% from 15 to 180 minutes.

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Section: Discussionsupporting

confidence: 88%

“…Of course, differences in climate conditions caused by different terrain also contribute to different predictions. In addition, when comparing LSTM, many references analyzed the prediction results in different seasons 34,45,46 . The prediction accuracy is approximately 90% to 93%, which is similar to the prediction results in this article.…”

Section: Discussionsupporting

confidence: 81%

Ultra‐short‐term photovoltaic power forecasting of multifeature based on hybrid deep learning

Huang

Zhou

Yang

2021

Intl J of Energy Research

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“…However, although ANN and sky imaging methods can deliver excellent performance (Crisosto et al, 2018), the overall cost is increased by deploying and operating expensive equipment, adding further complexity by working with images. Feed-forward neural networks (FFNNs) that are not designed for use with sequential data and are not as performant as RNN can deliver reasonable performance (Yona et al, 2007;Gao et al, 2019). Another potential approach is to use the radiation classification coordinate method to find and select similar sub-time series that are further used as input in learning networks-based models where LSTM was proven to perform better than backpropagation neural network, radial basis neural network, and Elman neural network (Chen et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Solar Energy Production Forecast Using Standard Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit

Buturache

Stancu

2021

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Solar radiation is among the renewable resources on which modern society relies to partially replace the existing fossil fuel-based energy resources. Awareness of how the energy is produced must complement awareness of how it is consumed. In the economic context, the gains derive from predictability across the entire supply chain. This paper represents a compressive study on how standard recurrent neural networks, long short-term memory, and gated recurrent units can be used to forecast power production of photovoltaic (PV) systems. This approach can be used for other use cases in solar or even wind power prediction since it provides solid fundamentals for working with weather data and recurrent artificial neural networks, being the core of any smart grid management system. Few studies have explored how these models should be implemented, and even fewer have compared the outcomes of different model types. The data used consist of weather and power production data with a one-hour resolution. The data were further pre-processed to unveil the maximum information. The most effective model parameters were selected to make the forecast. Solar energy plays a key role among other renewable energy sources in the European Union’s climate action and the European Green Deal. Under these initiatives, important regulations are implemented and financial resources made available for those who possess the capabilities required to solve the open points. The much-needed predictability that gives the flexibility and robustness needed for deploying and adopting more renewable technologies can be ensured by utilizing a neural-based predictive approach.

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“…By the evaluation of the prediction performance and generation ability, the proposed model has some advantages of PV power prediction. Gao et.al [13] realized the prediction of the short-term power production in a large-scale photovoltaic plant. The result indicates that the LSTM is suitable to predict the PV power.…”

Section: State Of the Artmentioning

confidence: 99%

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

Wang

Liu

et al. 2020

IEEE Access

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Because of intermittence and fluctuation of photovoltaic (PV) power, it is difficult to enhance prediction accuracy. To sustain high-efficient operation of power system, this paper proposes a hybrid method to predict the short-term PV power. It consists of components separation of PV power, parameters optimization and reconstruction of prediction result. Firstly, the methods based on the identifying of feature frequency and mutual information maximum are proposed to optimize the mode number and penalty factor of VMD, respectively. The optimized VMD (OVMD) is used to decompose the complicated fluctuation components of PV power into single component. Then, the improved PSO (IPSO) based on non-linear inertia weight of anti-sine function is proposed to optimize the number of hidden layer nodes, learning rate and iteration number of LSTM network. The optimized LSTM is used to predict each single component of OVMD decomposition. Thirdly, the prediction result of each single component is reconstructed to obtain the final PV prediction power. The experiment result indicates that the prediction accuracy of the proposed method (OVMD-IPSO-LSTM) outperformances the other typical methods. By the improvement of the traditional method (VMD and PSO) and the parameter optimization of LSTM, this hybrid method makes a contribution to the prediction of short-term PV power.

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Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Cited by 32 publications

References 28 publications

Ultra‐short‐term photovoltaic power forecasting of multifeature based on hybrid deep learning

Ultra‐short‐term photovoltaic power forecasting of multifeature based on hybrid deep learning

Solar Energy Production Forecast Using Standard Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

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