Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach
TLDR
In this paper , an improved generally applicable stacked ensemble algorithm (DSE-XGB) is proposed utilizing two deep learning algorithms namely artificial neural network (ANN) and long short-term memory (LSTM) as base models for solar energy forecast.About:
This article is published in Energy.The article was published on 2022-02-01 and is currently open access. It has received 58 citations till now. The article focuses on the topics: Photovoltaic system & Computer science.read more
Citations
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Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction
TL;DR: In this article , an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural Network (AUTO-NN) with Restricted Boltzmann feature extraction is proposed.
Journal ArticleDOI
Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications
TL;DR: In this paper , a comprehensive and systematic review of virtual collection of distributed photovoltaic systems (DPVS) is provided, including the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference.
Journal ArticleDOI
Grid Integration Challenges and Solution Strategies for Solar PV Systems: A Review
TL;DR: In this paper , the challenges reported due to the grid integration of solar PV systems and relevant proposed solutions are reviewed and discussed, including non-dispatchability, power quality, angular and voltage stability, reactive power support, and fault ride-through capability related to solar PV system grid integration.
Journal ArticleDOI
Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model
Abdallah Abdellatif,Hamza Mubarak,Shameem Ahmad,Tofael Ahmed,Gm. Shafiullah,Ahmad Hammoudeh,Hamdan Abdellatef,Hassan Muwafaq gheni +7 more
TL;DR: A stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead is proposed, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models.
Journal ArticleDOI
Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction
K.Y. Sudharshan,Ch . Naveen,Pradeep Vishnuram,Damodhara Venkata Siva Krishna Rao Krishna Rao Kasagani,Benedetto Nastasi +4 more
TL;DR: In this article , the authors present a review of various models in solar irradiance and power estimation which are tabulated by classification types mentioned, with an ultimate objective of minimizing uncertainty in forecasting.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
Journal ArticleDOI
Original Contribution: Stacked generalization
TL;DR: The conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate.
Journal ArticleDOI
Applications of artificial neural-networks for energy systems
TL;DR: In this paper, the authors present various applications of neural networks in energy problems in a thematic rather than a chronological or any other way, including modeling and design of a solar steam generating plant, estimation of a parabolic-trough collector's intercept factor and local concentration ratio, and performance prediction of solar water-heating systems.
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