ArticleComputational intelligence and neuroscience2022
Photovoltaic Power Generation Forecasting Using a Novel Hybrid Intelligent Model in Smart Grid.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exponential growth of electrical demand and the integration of renewable energy sources (RES) brought new challenges in the traditional grid about energy quality. The transition from traditional grid to smart grid is the best solution which provides necessary tools and information and communication technologies (ICT) for service enhancement. In this study, variation of energy demand and some factors of atmospheric change are considered to forecast production of photovoltaic energy that can be adapted for evolution of consumption in smart grid. The contribution of this study concerns a novel optimized hybrid intelligent model made of the artificial neural network (ANN), support vector machine (SVM), and particle swarm optimization (PSO) implemented for long term photovoltaic (PV) power generation forecasting based on real data of consumption and climate factors of the city of Douala in Cameroon. The accuracy of this model is evaluated using the coefficients such as the mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and regression coefficient (R). Using this novel hybrid technique, the MSE, RMSE, MAPE, MAE, and
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.