Q1. What are the contributions in this paper?
In this paper, a hybrid PSO-ANFIS approach was proposed for short-term wind power prediction in Portugal.
A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal
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TLDR
A new hybrid approach is proposed, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal, where significant improvements regarding forecasting accuracy are attainable.About:
This article is published in Energy Conversion and Management.The article was published on 2011-01-01 and is currently open access. It has received 153 citations till now. The article focuses on the topics: Wind power & Adaptive neuro fuzzy inference system.read more
Citations
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Journal ArticleDOI
A review of combined approaches for prediction of short-term wind speed and power
TL;DR: In this article, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance, and an up-to-date annotated bibliography of the wind forecasting literature is presented.
Journal ArticleDOI
Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey
Dervis Karaboga,Ebubekir Kaya +1 more
TL;DR: The heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study and it has been observed that there is a trend toward heuristic based ANfIS training algorithms for better performance recently.
Journal ArticleDOI
Applications of fuzzy logic in renewable energy systems – A review
TL;DR: An attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications and indicates that fuzzy based models provide realistic estimates.
Journal ArticleDOI
Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information
TL;DR: In this article, a hybrid forecasting model combining wavelet transform, particle swarm optimization and support vector machine (Hybrid WT-PSO-SVM) was proposed for short-term (one-day-ahead) generation power forecasting of a real microgrid PV system.
Journal ArticleDOI
Applications of artificial neural networks for thermal analysis of heat exchangers – A review
TL;DR: In this paper, the authors reviewed the applications of ANN for thermal analysis of heat exchangers and highlighted the limitations of ANN in this field and its further research needs in the field.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
A review on the forecasting of wind speed and generated power
TL;DR: A bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting and further direction for additional research and application is proposed.
Journal ArticleDOI
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models
TL;DR: A novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models is proposed, where the historical and usually ill-behaved price series is decomposed using the wavelets to reconstruct the future behavior of the price series and therefore to forecast prices.
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Day-ahead wind speed forecasting using f-ARIMA models
TL;DR: In this article, the authors examined the use of fractional-ARIMA or f-ARAMA models to model, and forecast wind speeds on the day-ahead and two-day-ahead (48 h) horizons.