ArticleScientific reports2025
Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- SIFA: A two-stage adaptive ensemble framework for solar irradiance forecasting using a wrapper-based feature selection and chaotic manta ray optimization.Scientific reports · 2026Article
- Optimized cross-attention GRU-transformer network with sooty tern algorithm for accurate solar power forecasting.Scientific reports · 2026Article
- LSTM-CNN hybrid model for E-commerce talent demand prediction and intelligent program optimization in vocational colleges under the double first-class initiative.Scientific reports · 2026Article
- Machine and deep learning-based forecast of Tehran's accelerating nighttime surface heat island crisis.Scientific reports · 2026Article
- Article
- A hybrid TimeGAN-xLSTM-Transformer framework for photovoltaic power forecasting under complex environmental conditions.Scientific reports · 2026Article
- Diagnosis of misalignment faults using the DTCWT-RCMFDE and LSSVM algorithms.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR) prediction models. The prediction is ensured for a period ranging from a few hours to several days of the year. These models are derived from four machine learning methods, namely the Feed-forward Back Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method, Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combines Convolutional Neural Networks and Long Short-Term Memory networks. This combination results in the CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Normalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by each model above and the actual output, pre-recorded in the experimental trial. The experimental results consistently show the power of the CNN-LSTM model compared to the remaining models in terms of accuracy and reliability. This is due to its lower error rate and higher detection coefficient (R
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.