A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables

Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network

“…The CLSTM model, constructed by integrating CNN and LSTM, had been used elsewhere in natural language processing where emotions were analysed with text inputs , in speech processing where voice search tasks were performed using CLDNN combining CNN, LSTM and DNN (Sainath et al 2015), in video processing with CNN and Bi-directional LSTM models built to recognize human actions in video sequences (Ullah et al 2017), in the medical area where the CNN-LSTM method was developed to detect arrhythmias in electrocardiograms (Oh et al 2018) and in industrial areas where a convolutional bidirectional LSTM model was designed to predict tool wearing (Zhao et al 2017). Other studies with CLSTM are evident, for example, time series application for prediction of residential energy consumption (Kim and Cho 2019; Ullah et al 2019), solar radiation prediction (Lee et al 2018;Wang, et al 2018;Ghimire 2019a;Gao et al 2020) and wind speed prediction (Hong and Satriani 2020; Jaseena and Kovoor 2021; Meka et al 2021) as well as stock market applications in the prediction of share prices (Vidal and Kristjanpoller 2020;Yadav et al 2020). In the solar radiation forecasting area, the study of Ghimire et al (2019a) has developed a CLSTM model and compared its performance against the CNN, LSTM and DNN-based models, showing that the CLSTM model outperformed the standalone version of both CNN and LSTM models.…”

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

“…A classification strategy may forecast a non-discrete value; however, the constant value is in the form of probability. On the other hand, the regression techniques may forecast a non-continuous value, yet the non-continuous as a number amount [19].…”

“…[18]. These variables are also correlated with each other that results in high wind power fluctuation and eventually causes difficulties to achieve satisfactory outcomes in wind power forecasting [19]. To enhance the reliability of the power supply system as well as address the intermittency characteristics of wind power, the reserve capacity of the power supply must be ensured to deliver the continuous power supply when the wind power is inadequate [20].…”

Forecasting Photosynthetic Photon Flux Density Under Cloud Effects: Novel Predictive Model Using Convolutional Neural Network Integrated With Long Short-term Memory Network

“…The CLSTM model, constructed by integrating CNN and LSTM, had been used elsewhere in natural language processing where emotions were analysed with text inputs [49], in speech processing where voice search tasks were performed using CLDNN combining CNN, LSTM and DNN [50], in video processing with CNN and Bidirectional LSTM models built to recognize human actions in video sequences [51], in the medical area where the CNN-LSTM method was developed to detect arrhythmias in electrocardiograms [52] and in industrial areas where a convolutional bi-directional LSTM model was designed to predict tool wearing [53]. Other studies with CLSTM are evident, for example, time series application for prediction of residential energy consumption [54] [55], solar radiation prediction [43,[56][57][58] and wind speed prediction [59][60][61] as well as stock market applications in the prediction of share prices [62,63]. In the solar radiation forecasting area, the study of Ghimire et al [43] has developed a CLSTM model and compared its performance against the CNN, LSTM and DNN-based models,…”