Journal ArticleDOI
Solar photovoltaic generation forecasting methods: A review
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TLDR
In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.About:
This article is published in Energy Conversion and Management.The article was published on 2018-01-15. It has received 539 citations till now. The article focuses on the topics: Photovoltaic system & Electric power system.read more
Citations
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Journal ArticleDOI
A review of deep learning for renewable energy forecasting
TL;DR: A comprehensive and extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness, efficiency and application potential and the current research activities, challenges, and potential future research directions are explored.
Journal ArticleDOI
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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A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework
TL;DR: Simulation results show that the proposed forecasting method with time correlation modification (TCM) is more accurate than the individual LSTM-RNN model, and the performance of the forecasting model can be further improved for those days with accurate daily pattern predictions under the proposed PDPP framework.
Journal ArticleDOI
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
TL;DR: A systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods to assist researchers in choosing the best forecasting technique for future research.
Journal ArticleDOI
Photovoltaic power forecasting based LSTM-Convolutional Network
Kejun Wang,Xiaoxia Qi,Hongda Liu +2 more
TL;DR: A hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction and shows that the hybrid prediction model has better prediction effect than the single prediction model.
References
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Journal ArticleDOI
Time Series Analysis: Forecasting and Control
Book
Introduction to Time Series Analysis and Forecasting
TL;DR: The nature and uses of Forecasting, and some Comments on Practical Implementation and use of Statistical Forecasting Techniques, are outlined.
Journal ArticleDOI
Review of photovoltaic power forecasting
J. Antonanzas,Natalia Osorio,Rodrigo Escobar,Ruben Urraca,Francisco Javier Martinez-de-Pison,F. Antonanzas-Torres +5 more
TL;DR: This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.
Journal ArticleDOI
Solar forecasting methods for renewable energy integration
TL;DR: In this article, the authors review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.