ArticleSensors (Basel, Switzerland)2023
Solar Power Prediction Using Dual Stream CNN-LSTM Architecture.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comparative analysis of CNN-LSTM and CNN-BiLSTM hybrid deep learning models for solar radiation prediction.Scientific reports · 2026Article
- Interpretable ultra-short-term photovoltaic power forecasting with multi-scale temporal modeling and variable-wise attention.Scientific reports · 2026Article
- Attention-Enhanced CNN-LSTM with Spatial Downscaling for Day-Ahead Photovoltaic Power Forecasting.Sensors (Basel, Switzerland) · 2026Article
- TCN-LSTM-AM Short-Term Photovoltaic Power Forecasting Model Based on Improved Feature Selection and APO.Sensors (Basel, Switzerland) · 2025Article
- AI-Integrated autonomous robotics for solar panel cleaning and predictive maintenance using drone and ground-based systems.Scientific reports · 2025Article
- Active learning regression quality prediction model and grinding mechanism for ceramic bearing grinding processing.PloS one · 2025Article
- Forecasting a Short-Term Photovoltaic Power Model Based on Improved Snake Optimization, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network.Sensors (Basel, Switzerland) · 2024Article
- Intelligent IoT Platform for Multiple PV Plant Monitoring.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network (CNN) and long short-term nemory (LSTM) network followed by a self-attention mechanism network (DSCLANet). Here, CNN is used to learn spatial patterns and LSTM is incorporated for temporal feature extraction. The output spatial and temporal feature vectors are then fused, followed by a self-attention mechanism to select optimal features for further processing. Finally, fully connected layers are incorporated for short-term solar power prediction. The performance of DSCLANet is evaluated on DKASC Alice Spring solar datasets, and it reduces the error rate up to 0.0136 MSE, 0.0304 MAE, and 0.0458 RMSE compared to recent state-of-the-art methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.