Journal ArticleDOI
Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines
TLDR
A fairly simple nonlinear regression model known as multivariate adaptive regression splines (MARS) is suggested, as an alternative to forecasting of solar power output, that maintains simplicity of the classical multiple linear regression (MLR) model while possessing the capability of handling nonlinearity.About:
This article is published in Applied Energy.The article was published on 2016-10-15. It has received 120 citations till now. The article focuses on the topics: Multivariate adaptive regression splines & Linear model.read more
Citations
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Journal ArticleDOI
Forecasting of photovoltaic power generation and model optimization: A review
Utpal Kumar Das,Kok Soon Tey,Mohammadmehdi Seyedmahmoudian,Saad Mekhilef,Moh Yamani Idna Idris,Willem Van Deventer,Ben Horan,Alex Stojcevski +7 more
TL;DR: In this paper, a comprehensive and systematic review of the direct forecasting of PV power generation is presented, where the importance of the correlation of the input-output data and the preprocessing of model input data are discussed.
Online Short-term Solar Power Forecasting
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
Journal ArticleDOI
Solar photovoltaic generation forecasting methods: A review
TL;DR: 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.
Journal ArticleDOI
History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining
TL;DR: This paper presents a preliminary study on how to review solar irradiance and photovoltaic power forecasting using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting.
Journal ArticleDOI
A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network
Kejun Wang,Xiaoxia Qi,Hongda Liu +2 more
TL;DR: The results showed that when the input sequence is increased, the accuracy of the model is improved, and the prediction effect of the hybrid model is the best, followed by that of convolutional neural network.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
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Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
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Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
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Time Series Analysis: Forecasting and Control
TL;DR: Time Series Analysis and Forecasting: principles and practice as mentioned in this paper The Oxford Handbook of Quantitative Methods, Vol. 3, No. 2: Statistical AnalysisTime-Series ForecastingPractical Time-Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series analysisTime Series analysisElements of Nonlinear Time Series analyses and forecastingTime series analysis and forecasting by Example.
Journal ArticleDOI
Online short-term solar power forecasting
TL;DR: In this paper, a two-stage method is proposed to forecast hourly values of solar power for horizons of up to 36 h. The results indicate that for forecasts up to 2 hours ahead, the most important input is the available observations of PV power, while for longer horizons numerical weather predictions (NWPs) are the more important input.