A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

Wu, Kaitong; Peng, Xiangang; Li, Zilu; Cui, Wenbo; Yuan, Haoliang; Lai, Chun Sing; Lai, Loi Lei

doi:10.3390/en15155410

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…The developed algorithm proved its strong ability to support energy efficiency management more efficiently than other neural network models. Wu et al proposed novel BiLSTM and trend feature extraction to reduce the short-term household predicting error, combined with the variational model decomposition and wavelet threshold denoising algorithms (Wu et al, 2023). The experiment results with the United States region proved the accuracy improvement for household load prediction with more stable and precise outcomes.…”

Section: Previous Workmentioning

confidence: 99%

Hourly load prediction based feature selection scheme and hybrid CNN‐LSTM method for building's smart solar microgrid

Da,

Cho,

Thanh

2024

Expert Systems

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The short‐term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the rooftop with the battery management system (BMS) to enhance energy efficiency management. However, the traditional methodologies only processed the weather parameters and power demand information for short‐term load prediction, ignoring the collected data from SSM and BMS by the advanced metering infrastructures (AMI), which probably improved prediction accuracy. In this research, many accumulated data of building and SSM are collected before methodology implementation. Considering the diversities of accumulated parameters from SSM and BMS, an adaptive convolution neural network long short‐term memory (CNN‐LSTM) is proposed for hourly electrical load prediction. The CNN could extract the critical large‐scale input feature, while the LSTM could achieve better accurate forecasts. The Pearson correlation matrix is calculated for the feature selection scheme from different data units. The hyperparameter tuning is utilized for obtaining the optimized hybrid CNN‐LSTM algorithm. The K‐fold cross‐validation is employed for deep learning algorithm verification, which includes LSTM, GRU, CNN, and Bi‐LSTM methodologies. The results prove that the hybrid CNN‐LSTM achieved outperformed improvements, which are 20.57%, 29.63%, 19.06% in MSE, MAE, MAPE, and 21.24%, 22.02%, 3.82% in validating MSE, MAE, MAPE, respectively. The hybrid CNN and LSTM combined with the feature selection scheme achieve superior predicting accuracies, proving the adaptability ability for integrating into the energy management system (EMS) of the building's SSM.

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Section: Previous Workmentioning

confidence: 99%

Hourly load prediction based feature selection scheme and hybrid CNN‐LSTM method for building's smart solar microgrid

Da,

Cho,

Thanh

2024

Expert Systems

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“…The core idea of VMD is to construct and solve variational problems. The specific rationale is described in the literature [9], [10] .LSTM neural network specific calculation formula is as follows [11,12] :…”

Section: Relevant Theoriesmentioning

confidence: 99%

Optimize VMD-LSTM photovoltaic force combination prediction model based on SSA parameter optimization

Yang,

Ma,

Zhao

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…Since the BILSTM consists of an LSTM in both directions of the information flow, the forecast accuracy is enhanced by considering both historical information (past) and trend information (future). In [ 42 , 43 ], the BiLSTM method is compared to others and it shows a higher accuracy by having the lowest errors RMSE, MAE and MAPE, making the BiLSTM an effective and reliable method for the PV generation forecast.…”

Section: Related Workmentioning

confidence: 99%

Application of AI for Short-Term PV Generation Forecast

Rocha,

Fiorotti,

Fardin

et al. 2023

Sensors

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The efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).

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A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

Cited by 8 publications

References 33 publications

Hourly load prediction based feature selection scheme and hybrid CNN‐LSTM method for building's smart solar microgrid

Hourly load prediction based feature selection scheme and hybrid CNN‐LSTM method for building's smart solar microgrid

Optimize VMD-LSTM photovoltaic force combination prediction model based on SSA parameter optimization

Application of AI for Short-Term PV Generation Forecast

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