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
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TLDR
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.About:
This article is published in Energy.The article was published on 2022-01-15 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Prediction interval & Photovoltaic system.read more
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Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction
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Computational Solar Energy - Ensemble Learning Methods for Prediction of Solar Power Generation based on Meteorological Parameters in Eastern India
TL;DR: In this article , the impact of weather parameters on solar PV power generation is estimated by several ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time.
Proceedings ArticleDOI
A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids
Rituraj Rituraj,D. Ecker +1 more
TL;DR: In this article , a comprehensive investigation with the aid of PRISMA had been conducted, which revealed a significant increase in the popularity of this deep learning method in smart grid applications.
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Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources
TL;DR: In this article , an 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database, and statistical analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown.
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An Insight of Deep Learning Based Demand Forecasting in Smart Grids
TL;DR: In this paper , the authors provide an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collect some experiences of the use of deep learning techniques, for demand forecasting purposes.
References
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Review of solar irradiance forecasting methods and a proposition for small-scale insular grids
TL;DR: In this article, the authors present an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs.
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Assessment of forecasting techniques for solar power production with no exogenous inputs
TL;DR: In this paper, the authors evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1MWp, single-axis tracking, photovoltaic power plant operating in Merced, California.