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

Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting DOI
Song Ding, Ruojin Li, Shu Wu

et al.

Applied Energy, Journal Year: 2021, Volume and Issue: 298, P. 117114 - 117114

Published: June 8, 2021

Language: Английский

Citations

90
A State-of-Art-Review on Machine-Learning Based Methods for PV DOI Creative Commons
Giuseppe Marco Tina, Cristina Ventura, Sergio Ferlito

et al.

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(16), P. 7550 - 7550

Published: Aug. 17, 2021

In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. this scenario, machine learning (ML), a subset of AI techniques, provides machines ability to programmatically learn from data model system while adapting new situations as they more by are ingesting (on-line training). During last years, many papers have been published concerning ML field solar systems. This paper presents state art models applied energy’s forecasting i.e., for irradiance and power production (both point interval or probabilistic forecasting), electricity price energy demand forecasting. Other into photovoltaic (PV) taken account modelling PV modules, design parameter extraction, tracking maximum (MPP), systems efficiency optimization, PV/Thermal (PV/T) Concentrating (CPV) parameters’ optimization improvement, anomaly detection management PV’s storage While review already exist regard, usually focused only on one specific topic, gathered all most relevant different fields. The gives an overview recent promising used

Language: Английский

Citations

76
An unequal adjacent grey forecasting air pollution urban model DOI
Leping Tu, Yan Chen

Applied Mathematical Modelling, Journal Year: 2021, Volume and Issue: 99, P. 260 - 275

Published: July 1, 2021

Language: Английский

Citations

69
Predictions and mitigation strategies of PM2.5 concentration in the Yangtze River Delta of China based on a novel nonlinear seasonal grey model DOI
Weijie Zhou, Xiaoli Wu, Song Ding

et al.

Environmental Pollution, Journal Year: 2021, Volume and Issue: 276, P. 116614 - 116614

Published: Feb. 7, 2021

Language: Английский

Citations

68
Short-Term Photovoltaic Power Forecasting Based on VMD and ISSA-GRU DOI Creative Commons
Pengyun Jia, Haibo Zhang, Xinmiao Liu

et al.

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 105939 - 105950

Published: Jan. 1, 2021

Photovoltaic (PV) power generation is affected by many meteorological factors and environmental factors, which has obvious intermittent, random, volatile characteristics. To improve the accuracy of short-term PV prediction, a hybrid model (VMD-ISSA-GRU) based on variational mode decomposition (VMD), improved sparrow search algorithm (ISSA) gated recurrent unit (GRU) proposed. First all, time series decomposed into different subsequences VMD to reduce non-stationarity original data. Then, main affecting are obtained using correlation coefficients Spearman Pearson, reduces computational complexity model. Finally, GRU network optimized ISSA used predict all residual error VMD, prediction results reconstructed. The show that VMD-ISSA-GRU stronger adaptability higher than other traditional models. mean absolute (MAE) in whole test set 1.0128 kW, root square (RMSE) 1.5511 R adj 2 can reach 0.9993.

Language: Английский

Citations

67
Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm DOI
Jianzhou Wang, Yilin Zhou, Zhiwu Li

et al.

Applied Energy, Journal Year: 2022, Volume and Issue: 312, P. 118725 - 118725

Published: Feb. 18, 2022

Language: Английский

Citations

65
A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting DOI

Xinbo He,

Yong Wang, Yuyang Zhang

et al.

Applied Energy, Journal Year: 2022, Volume and Issue: 325, P. 119854 - 119854

Published: Aug. 24, 2022

Language: Английский

Citations

65
A novel composite forecasting framework by adaptive data preprocessing and optimized nonlinear grey Bernoulli model for new energy vehicles sales DOI
Song Ding, Ruojin Li, Shu Wu

et al.

Communications in Nonlinear Science and Numerical Simulation, Journal Year: 2021, Volume and Issue: 99, P. 105847 - 105847

Published: April 3, 2021

Language: Английский

Citations

63
A novel structural adaptive discrete grey prediction model and its application in forecasting renewable energy generation DOI
Wuyong Qian,

Aodi Sui

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 186, P. 115761 - 115761

Published: Aug. 17, 2021

Language: Английский

Citations

61
Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model DOI
Song Ding, Zui Tao,

Huahan Zhang

et al.

Energy, Journal Year: 2021, Volume and Issue: 239, P. 121928 - 121928

Published: Aug. 30, 2021

Language: Английский

Citations

61