ArticleScientific reports2024
An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- High-resolution photovoltaic power forecasting using machine learning models under seasonal and stress conditions.Scientific reports · 2026Article
- Resilient frequency stabilization of renewable-penetrated microgrids via deep learning and machine learning.Scientific reports · 2026Article
- Diagnosis of misalignment faults using the DTCWT-RCMFDE and LSSVM algorithms.Scientific reports · 2025Article
- Self-adaptive evolutionary neural networks for high-precision short-term electric load forecasting.Scientific reports · 2025Article
- Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria.Scientific reports · 2025Article
- Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems.Scientific reports · 2025Article
- Application of explainable machine learning for estimating direct and diffuse components of solar irradiance.Scientific reports · 2025Article
- Assessment of compressive strength of eco-concrete reinforced using machine learning tools.Scientific reports · 2025Article
- Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria.Scientific reports · 2025Article
- Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems.Scientific reports · 2024Article
- Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques.Scientific reports · 2024Article
- Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations.Scientific reports · 2024Article
- Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.Scientific reports · 2024Article
- Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply-demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454-1.54] for the three studied PV stations.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.