PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production

[1]  R. Bird,et al.  Simplified clear sky model for direct and diffuse insolation on horizontal surfaces , 1981 .

[2]  J. Duffie,et al.  Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation , 1982 .

[3]  J. Olseth,et al.  A model for the diffuse fraction of hourly global radiation , 1987 .

[4]  P. Ineichen,et al.  Dynamic global-to-direct irradiance conversion models , 1992 .

[5]  John Boland,et al.  Modelling of diffuse solar fraction with multiple predictors , 2010 .

[6]  Hui Liu,et al.  Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction , 2012 .

[7]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[8]  Nicholas A. Engerer.  Minute resolution estimates of the diffuse fraction of global irradiance for southeastern Australia , 2015 .

[9]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[10]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[13]  Dit-Yan Yeung,et al.  Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model , 2017, NIPS.

[14]  J. Boland,et al.  Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction , 2018, Renewable Energy.

[15]  Xiang Yu,et al.  Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network , 2019, Applied Energy.

[16]  Xiaoxia Qi,et al.  A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network , 2019, Applied Energy.

[17]  M. J. Costa,et al.  Prediction of diffuse horizontal irradiance using a new climate zone model , 2019, Renewable and Sustainable Energy Reviews.

[18]  Li Li,et al.  Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network , 2019, Applied Energy.

[19]  Chao Li,et al.  Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization , 2020, Journal of Cleaner Production.

[20]  Yi-Ming Wei,et al.  An adaptive hybrid model for day-ahead photovoltaic output power prediction , 2020 .

[21]  Kwanho Kim,et al.  PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information , 2020 .

[22]  Oriol Gomis-Bellmunt,et al.  A review of energy storage technologies for large scale photovoltaic power plants , 2020 .

[23]  J. Catalão,et al.  A minutely solar irradiance forecasting method based on real-time sky image-irradiance mapping model , 2020, Energy Conversion and Management.

[24]  Sofiane Khadraoui,et al.  Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach , 2020 .

[25]  Shanlin Yang,et al.  A hybrid deep learning model for short-term PV power forecasting , 2020 .

[26]  Jie Zhang,et al.  Probabilistic solar power forecasting based on weather scenario generation , 2020, Applied Energy.

[27]  V. Sreeram,et al.  A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization , 2020 .

[28]  M. J. Mayer,et al.  Extensive comparison of physical models for photovoltaic power forecasting , 2020 .

[29]  Wansi Yin,et al.  A novel non-iterative correction method for short-term photovoltaic power forecasting , 2020 .

[30]  Robert S. Balog,et al.  Time series forecasting of solar power generation for large-scale photovoltaic plants , 2020 .

[31]  Soo-Bin Han,et al.  Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells , 2020, Applied Energy.

[32]  Ratnesh K. Sharma,et al.  Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model , 2020, IEEE Transactions on Industry Applications.

[33]  Yong Tang,et al.  Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions , 2020, Applied Energy.

[34]  Zhao Zhen,et al.  A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework , 2020 .

[35]  Qian Huang,et al.  Improved quantile convolutional neural network with two-stage training for daily-ahead probabilistic forecasting of photovoltaic power , 2020 .

[36]  Lijun Yu,et al.  Optimization of photovoltaic battery swapping station based on weather/traffic forecasts and speed variable charging , 2020 .

[37]  Sayalee G. Mahajan,et al.  Persistent, single-polarity energy harvesting from ambient thermal fluctuations using a thermal resonance device with thermal diodes , 2020 .

[38]  E. Trutnevyte,et al.  Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models , 2020 .

[39]  Sungwhan Kim,et al.  Design optimization of large-scale attached cultivation of Ettlia sp. to maximize biomass production based on simulation of solar irradiation , 2020 .

[40]  Dolaana Khovalyg,et al.  Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition , 2020 .

[41]  Yingfeng Zhang,et al.  Big data driven predictive production planning for energy-intensive manufacturing industries , 2020 .

[42]  Rishee K. Jain,et al.  SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods , 2020 .

[43]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[44]  Spyros Theocharides,et al.  Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing , 2020 .

[45]  Nanrun Zhou,et al.  Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine , 2020, Energy.

[46]  P. F. Ribeiro,et al.  Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting , 2021 .

[47]  Yizhen Liu,et al.  Review of interface solar-driven steam generation systems: High-efficiency strategies, applications and challenges , 2021 .

[48]  Zhao Yang Dong,et al.  Integrated planning of internet data centers and battery energy storage systems in smart grids , 2021 .