Journal ArticleDOI
Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power
Fermín Rodríguez,Fermín Rodríguez,Fernando Martín,Fernando Martín,Luis Fontán,Luis Fontán,Ainhoa Galarza,Ainhoa Galarza +7 more
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TLDR
The results indicate that the developed forecaster can be integrated into photovoltaic generators’ to predict their output power, thus promoting their inclusion in the main power network.About:
This article is published in Energy.The article was published on 2021-08-15. It has received 26 citations till now. The article focuses on the topics: Photovoltaic system.read more
Citations
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Journal ArticleDOI
Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control
Fermín Rodríguez,Fermín Rodríguez,Ainhoa Galarza,Ainhoa Galarza,Juan C. Vasquez,Josep M. Guerrero +5 more
TL;DR: A new model for predicting photovoltaic generators' output power confidence interval 10 min ahead is developed, based on deep learning, mathematical probability density functions and meteorological parameters, which has been validated with a real data series collected from Spanish meteorological stations.
Journal ArticleDOI
Forecasting intra-hour solar photovoltaic energy by assembling wavelet based time-frequency analysis with deep learning neural networks
Journal ArticleDOI
Forecasting intra-hour solar photovoltaic energy by assembling wavelet based time-frequency analysis with deep learning neural networks
TL;DR: In this article , a wavelet based time-frequency analysis of the used data with deep learning neural networks to forecast solar irradiation, in next 10 min, to compute solar photovoltaic generation.
Journal ArticleDOI
Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control
TL;DR: In this paper , the authors developed a new model for predicting photovoltaic generators' output power confidence interval 10 min ahead, based on deep learning, mathematical probability density functions and meteorological parameters.
Journal ArticleDOI
Solar Irradiance Forecasting Using Dynamic Ensemble Selection
Domingos S. de O. Santos,Paulo S. G. de Mattos Neto,João Fausto Lorenzato de Oliveira,Hugo Siqueira,Tathiana M. Barchi,Aranildo R. Lima,Francisco Madeiro,Douglas A. P. Dantas,Attilio Converti,A. C. Pereira,José Bione de Melo Filho,Manoel Henrique da Nóbrega Marinho +11 more
TL;DR: A heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance with an overall accuracy that is superior to the single models in terms of five well-known error metrics is proposed.
References
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Journal ArticleDOI
Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control
Fermín Rodríguez,Fermín Rodríguez,Alice Fleetwood,Alice Fleetwood,Ainhoa Galarza,Ainhoa Galarza,Luis Fontán,Luis Fontán +7 more
TL;DR: The ability to predict the parameters that are involved in solar energy production will allow us to estimate the future power production in order to optimise grid control and the accuracy of the tool is sufficient enough to be installed in systems which have integrated solar generators.
Journal ArticleDOI
Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks
Jiacong Cao,Xingchun Lin +1 more
TL;DR: A diagonal recurrent wavelet neural network (DRWNN) is newly established in this paper to perform fine forecasting of hourly and daily global solar irradiance and comparisons between irradiation models show that the DRWNN models are definitely more accurate.
Journal ArticleDOI
Solar Forecasting: Methods, Challenges, and Performance
Aidan Tuohy,John Zack,Sue Ellen Haupt,Justin Sharp,Mark Ahlstrom,Skip Dise,Eric P. Grimit,Corinna Möhrlen,Matthias Lange,Mayte Garcia Casado,Jon Black,Melinda Marquis,J. Craig Collier +12 more
TL;DR: The deployment of solar-based electricity generation, especially in the form of photovoltaics (PVs), has increased markedly in recent years due to a wide range of factors including concerns over greenhouse gas emissions, supportive government policies, and lower equipment costs.
Journal ArticleDOI
Spatial-Temporal Solar Power Forecasting for Smart Grids
TL;DR: This paper presents a new spatial-temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers, leading to an improvement on average between 8% and 10%.
Journal ArticleDOI
Short term solar irradiance forecasting using a mixed wavelet neural network
TL;DR: A mixed wavelet neural network (WNN) is proposed in this paper for short-term solar irradiance forecasting, with initial application in tropical Singapore, and results show that WNN delivers better prediction skill when compared with other forecasting techniques.