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IEEE transactions on industry applications, v.56 no.6, 2020년, pp.7185 - 7192
Zhang, Yue (GE Digital, Bothell, WA, USA) , Qin, Chuan (School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA) , Srivastava, Anurag K. (School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA) , Jin, Chenrui (NEC Labs America, Cupertino, CA, USA) , Sharma, Ratnesh K. (NEC Labs America, Cupertino, CA, USA)
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Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. However, uncertain wea...
J Mach Learn Res Dropout: A simple way to prevent neural networks from overfitting hinton 2014 15 1929
Kraas, B., Schroedter-Homscheidt, M., Madlener, R.. Economic merits of a state-of-the-art concentrating solar power forecasting system for participation in the Spanish electricity market. Solar energy, vol.93, 244-255.
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Wang, G., Su, Y., Shu, L.. One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models. Renewable energy, vol.96, no.1, 469-478.
Li, Y., He, Y., Su, Y., Shu, L.. Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines. Applied energy, vol.180, 392-401.
Massidda, Luca, Marrocu, Marino. Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany. Solar energy, vol.146, 141-149.
10.1109/TENCON.2017.8228038
Li, Ling-Ling, Cheng, Peng, Lin, Hsiung-Cheng, Dong, Hao. Short-term output power forecasting of photovoltaic systems based on the deep belief net. Advances in mechanical engineering, vol.9, no.9, 168781401771598-.
Proc IEEE Int Conf Syst Man Cybern Deep learning for solar power forecasting—An approach using autoencoder and LSTM neural metworks gensler 0 2858
Abdel-Nasser, Mohamed, Mahmoud, Karar. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural computing & applications, vol.31, no.7, 2727-2740.
Lee, Donghun, Kim, Kwanho. Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information. Energies, vol.12, no.2, 215-.
Wang, Kejun, Qi, Xiaoxia, Liu, Hongda. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Applied energy, vol.251, 113315-.
Zang, Haixiang, Cheng, Lilin, Ding, Tao, Cheung, Kwok W., Liang, Zhi, Wei, Zhinong, Sun, Guoqiang. Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network. IET generation, transmission & distribution, vol.12, no.20, 4557-4567.
Aguiar, L. Mazorra, Pereira, B., Lauret, P., Díaz, F., David, M.. Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renewable energy, vol.97, 599-610.
10.2172/1069158
Amrouche, B., Le Pivert, X.. Artificial neural network based daily local forecasting for global solar radiation. Applied energy, vol.130, 333-341.
10.1109/IAS.2018.8544694
10.1109/IAS.2019.8912017
Sobri, Sobrina, Koohi-Kamali, Sam, Rahim, Nasrudin Abd.. Solar photovoltaic generation forecasting methods: A review. Energy conversion and management, vol.156, 459-497.
Ntl Renewable Energy Lab (NREL) NREL: How do high levels of wind and solar impact the grid? the western wind and solar integration study lew 2010
Monteiro, Claudio, Santos, Tiago, Fernandez-Jimenez, L., Ramirez-Rosado, Ignacio, Terreros-Olarte, M.. Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity. Energies, vol.6, no.5, 2624-2643.
10.1109/UPEC.2013.6714975
Solar industry research data 2019
Jie Shi, Wei-Jen Lee, Yongqian Liu, Yongping Yang, Peng Wang. Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines. IEEE transactions on industry applications, vol.48, no.3, 1064-1069.
Global Solar PV Installations Reach 109 GW in 2018—BNEF osborne 2019
Li, Pengtao, Zhou, Kaile, Lu, Xinhui, Yang, Shanlin. A hybrid deep learning model for short-term PV power forecasting. Applied energy, vol.259, 114216-.
Bengio, Y., Simard, P., Frasconi, P.. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, vol.5, no.2, 157-166.
Ospina, Juan, Newaz, Alvi, Faruque, M. Omar. Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model. IET renewable power generation, vol.13, no.7, 1087-1095.
Deep Learning with Python chollet 2017
Keras chollet 2015
National Center for Atmospheric Research University Corporation for Atmospheric Research Scientific documentation for the NMM solver janjic 2010
Yue Zhang, Beaudin, Marc, Taheri, Raouf, Zareipour, Hamidreza, Wood, David. Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators. IEEE transactions on smart grid, vol.6, no.5, 2253-2262.
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