| 1 |
Hybrid deep neural model for hourly solar irradiance forecasting
Huang X, Li Q, Tai YH, Chen ZQ, Zhang J, Shi JS, Gao BX, Liu WM
Renewable Energy, 171, 1041, 2021 |
| 2 |
Use of physics to improve solar forecast: Physics-informed persistence models for simultaneously forecasting GHI, DNI, and DHI
Liu WJ, Liu YG, Zhou X, Xie Y, Han YX, Yoo S, Sengupta M
Solar Energy, 215, 252, 2021 |
| 3 |
Solar irradiance forecasting models without on-site training measurements
Zambrano AF, Giraldo LF
Renewable Energy, 152, 557, 2020 |
| 4 |
Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks
Gao BX, Huang XQ, Shi JS, Tai YH, Zhang J
Renewable Energy, 162, 1665, 2020 |
| 5 |
Automated construction of clear-sky dictionary from all-sky imager data?
Shaffery P, Habte A, Netto M, Andreas A, Krishnan V
Solar Energy, 212, 73, 2020 |
| 6 |
Novel short term solar irradiance forecasting models
Akarslan E, Hocaoglu FO, Edizkan R
Renewable Energy, 123, 58, 2018 |
| 7 |
Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting
Verbois H, Rusydi A, Thiery A
Solar Energy, 173, 313, 2018 |
| 8 |
A novel method based on similarity for hourly solar irradiance forecasting
Akarslan E, Hocaoglu FO
Renewable Energy, 112, 337, 2017 |
| 9 |
Evaluation of statistical learning configurations for gridded solar irradiance forecasting
Gagne DJ, McGovern A, Haupt SE, Williams JK
Solar Energy, 150, 383, 2017 |
| 10 |
PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore
Nobre AM, Severiano CA, Karthik S, Kubis M, Zhao L, Martins FR, Pereira EB, Ruther R, Reindl T
Renewable Energy, 94, 496, 2016 |
