A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants DOI

Jian Du,

Jianqin Zheng, Yongtu Liang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 118, P. 105647 - 105647

Published: Nov. 28, 2022

Language: Английский

Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method DOI
Bo Gu,

Huiqiang Shen,

Xiaohui Lei

et al.

Applied Energy, Journal Year: 2021, Volume and Issue: 299, P. 117291 - 117291

Published: June 24, 2021

Language: Английский

Citations

117
Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy DOI
Yugui Tang, Kuo Yang, Shujing Zhang

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2022, Volume and Issue: 162, P. 112473 - 112473

Published: April 21, 2022

Language: Английский

Citations

92
LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method DOI

Yeming Dai,

Yanxin Wang, Mingming Leng

et al.

Energy, Journal Year: 2022, Volume and Issue: 256, P. 124661 - 124661

Published: June 28, 2022

Language: Английский

Citations

77
An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions DOI
Li Ye,

Deling Yang,

Yaoguo Dang

et al.

Energy, Journal Year: 2022, Volume and Issue: 249, P. 123681 - 123681

Published: March 9, 2022

Language: Английский

Citations

71
Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification DOI
Min Yu, Dongxiao Niu, Keke Wang

et al.

Energy, Journal Year: 2023, Volume and Issue: 275, P. 127348 - 127348

Published: April 6, 2023

Language: Английский

Citations

59
Energy Forecasting: A Comprehensive Review of Techniques and Technologies DOI Creative Commons
Aristeidis Mystakidis, Paraskevas Koukaras, Nikolaos Tsalikidis

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(7), P. 1662 - 1662

Published: March 30, 2024

Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved accuracy may make it easier to deal with imbalances between generation consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) architectures. For utilities, companies, consumers manage resources effectively educated decisions about consumption, EF is essential. many applications, Energy Load Forecasting (ELF), Generation (EGF), grid stability, accurate crucial. The state of the art examined this literature review, emphasising cutting-edge techniques technologies their significance for industry. gives an overview statistical, Machine Learning (ML)-based, Deep (DL)-based methods ensembles that form basis EF. Various time-series are explored, including sequence-to-sequence, recursive, direct forecasting. Furthermore, evaluation criteria reported, namely, relative absolute metrics Mean Absolute Error (MAE), Root Square (RMSE), Percentage (MAPE), Coefficient Determination (R2), Variation (CVRMSE), well Execution Time (ET), which used gauge prediction accuracy. Finally, overall step-by-step standard methodology often utilised problems presented.

Language: Английский

Citations

31
Deep learning model for short-term photovoltaic power forecasting based on variational mode decomposition and similar day clustering DOI
Meng Li, Wei Wang, Yan He

et al.

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 115, P. 109116 - 109116

Published: Feb. 15, 2024

Language: Английский

Citations

23
Short-term PV power prediction based on meteorological similarity days and SSA-BiLSTM DOI Creative Commons
Yikang Li, Wei Huang,

Keying Lou

et al.

Systems and Soft Computing, Journal Year: 2024, Volume and Issue: 6, P. 200084 - 200084

Published: Feb. 23, 2024

Accurate short-term photovoltaic (PV) power forecasting can reduce the un- certainty of PV generation, which is crucial for grid operation as well energy dispatch. Considering influence seasonal and meteorological factors on prediction, a predic- tion method based similarity day sparrow search algo- rithm bi-directional long memory network combination (SSA-BiLSTM) proposed. Firstly, correlation between generation calculated by using Pearson coefficients, getting strongly correlated affecting generation; afterwards,the historical data are clustered fuzzy C-means clustering to achieve similar clustering; then, best selected from according test features data, Forming training set with original BiLSTM network. SSA algorithm was used find optimal parameters. Finally, Using parameters construct prediction. The experiments were conducted plant in Xinjiang, also compared existing prediction algorithms.The results show that accuracy different weather conditions 33.1 %, 31.9 % 24.1 higher than under same intelligent optimization neural networks, 27.9 24.7 18.0 algorithms Therefore, this paper has better seasons conditions.

Language: Английский

Citations

17
A hybrid ensemble optimized BiGRU method for short-term photovoltaic generation forecasting DOI

Yeming Dai,

Weijie Yu, Mingming Leng

et al.

Energy, Journal Year: 2024, Volume and Issue: 299, P. 131458 - 131458

Published: April 27, 2024

Language: Английский

Citations

17
Forecasting the sales and stock of electric vehicles using a novel self-adaptive optimized grey model DOI
Song Ding, Ruojin Li

Engineering Applications of Artificial Intelligence, Journal Year: 2021, Volume and Issue: 100, P. 104148 - 104148

Published: Jan. 13, 2021

Language: Английский

Citations

93