A novel integrated photovoltaic power forecasting model based on variational mode decomposition and CNN-BiGRU considering meteorological variables

[1]  M. S. Nazir,et al.  An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction , 2022, Energy.

[2]  Tian Peng,et al.  Hybrid short-term runoff prediction model based on optimal variational mode decomposition, improved Harris hawks algorithm and long short-term memory network , 2022, Environmental Research Communications.

[3]  Eleonora Riva Sanseverino,et al.  A new method for forecasting energy output of a large-scale solar power plant based on long short-term memory networks a case study in Vietnam , 2021 .

[4]  Maryam Imani,et al.  Convolutional and recurrent neural network based model for short-term load forecasting , 2021 .

[5]  Kefei Zhang,et al.  Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks: A case study of Huaihai Economic Zone , 2020 .

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

[7]  Wenlong Fu,et al.  A compound framework for wind speed forecasting based on comprehensive feature selection, quantile regression incorporated into convolutional simplified long short-term memory network and residual error correction , 2020 .

[8]  Yue Zhang,et al.  Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method , 2020 .

[9]  Chu Zhang,et al.  Negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting , 2020 .

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

[11]  Sungrae Cho,et al.  Multiscale LSTM-Based Deep Learning for Very-Short-Term Photovoltaic Power Generation Forecasting in Smart City Energy Management , 2020, IEEE Systems Journal.

[12]  Gregory M. P. O'Hare,et al.  A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform , 2020, Neurocomputing.

[13]  Tao Ding,et al.  Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning , 2020, International Journal of Electrical Power & Energy Systems.

[14]  Sekyung Han,et al.  Optimal Energy Storage System Operation for Peak Reduction in a Distribution Network Using a Prediction Interval , 2020, IEEE Transactions on Smart Grid.

[15]  Ming-Lang Tseng,et al.  Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model , 2020 .

[16]  Kai Zhang,et al.  A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting , 2020 .

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

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

[19]  Fei Wang,et al.  Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting , 2019 .

[20]  Hui Liu,et al.  Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression , 2019 .

[21]  Chaoshun Li,et al.  Deep Learning Method Based on Gated Recurrent Unit and Variational Mode Decomposition for Short-Term Wind Power Interval Prediction , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Fan Zhang,et al.  A hybrid VMD-BiGRU model for rubber futures time series forecasting , 2019, Appl. Soft Comput..

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

[24]  M. Abdel-Nasser,et al.  Accurate photovoltaic power forecasting models using deep LSTM-RNN , 2019, Neural Computing and Applications.

[25]  Hai Zhou,et al.  A PV power interval forecasting based on seasonal model and nonparametric estimation algorithm , 2019, Solar Energy.

[26]  Zhong-kai Feng,et al.  A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm , 2019, Applied Energy.

[27]  Tao Ding,et al.  Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network , 2018, IET Generation, Transmission & Distribution.

[28]  Robin Girard,et al.  Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production , 2018, IEEE Transactions on Sustainable Energy.

[29]  Shaolong Sun,et al.  A decomposition-clustering-ensemble learning approach for solar radiation forecasting , 2018 .

[30]  Hailong Li,et al.  Forecasting Power Output of Photovoltaic System Using A BP Network Method , 2017 .

[31]  Yitao Liu,et al.  Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network , 2017 .

[32]  Chuanjin Yu,et al.  An improved Wavelet Transform using Singular Spectrum Analysis for wind speed forecasting based on Elman Neural Network , 2017 .

[33]  Salim Lahmiri,et al.  Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy Prices , 2017, IEEE Systems Journal.

[34]  Chu Zhang,et al.  A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting , 2017 .

[35]  He Jiang,et al.  Forecast of hourly global horizontal irradiance based on structured Kernel Support Vector Machine: A case study of Tibet area in China , 2017 .

[36]  Tian Peng,et al.  Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks , 2017 .

[37]  Luca Massidda,et al.  Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany , 2017 .

[38]  Marcelo Keese Albertini,et al.  Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter , 2017 .

[39]  Irena Koprinska,et al.  Univariate and multivariate methods for very short-term solar photovoltaic power forecasting , 2016 .

[40]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[41]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[42]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[43]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[44]  M. S. Nazir,et al.  A novel hybrid approach based on variational heteroscedastic Gaussian process regression for multi-step ahead wind speed forecasting , 2022, International Journal of Electrical Power & Energy Systems.

[45]  Jasmin Kevric,et al.  Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction , 2018, Biomed. Signal Process. Control..