Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control
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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.About:
This article is published in Energy.The article was published on 2022-01-01 and is currently open access. It has received 16 citations till now. The article focuses on the topics: Prediction interval & Photovoltaic system.read more
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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.
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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.
Proceedings ArticleDOI
A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids
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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Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting
Saad Mekhilef,Mehmet Fatih Küçük +1 more
TL;DR: In this article , an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting is discussed, and the performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution.
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.
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A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
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Green Energy Market Development in Germany: Effective Public Policy and Emerging Customer Demand
Rolf Wüstenhagen,Michael Bilharz +1 more
TL;DR: In this article, the authors investigated the relative importance of energy policy and green power marketing in shaping the renewable energy market and concluded that the German case can be used for policy design and market development in other countries.