Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN

Wu, Ji; Chan, Chon U

doi:10.1016/j.solener.2011.01.013

Cited by 274 publications

(65 citation statements)

References 29 publications

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“…Another method in considering uncertainties is adding a random disturbance to the average values of wind speed and solar irradiance [11]. Lujano-Rojas [13] and Ji [25] used a time series analysis to model wind speed and solar irradiance variations. To obtain accurate modelling of wind speed and solar irradiance variations, two methods are used to correlate historical data to known distributions and time series analysis using autoregressive moving average models (ARMA) [26].…”

Section: B Monte Carlo Simulationmentioning

confidence: 99%

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

Kamjoo

Maheri

Dizqah

et al. 2016

International Journal of Electrical Power & Energy Systems

198

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Citation: Kamjoo, Azadeh, Maheri, Alireza, Dizqah, Arash and Putrus, Ghanim (2016) Multiobjective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. International Journal of Electrical Power & Energy Systems, Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

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Section: B Monte Carlo Simulationmentioning

confidence: 99%

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

Kamjoo

Maheri

Dizqah

et al. 2016

International Journal of Electrical Power & Energy Systems

198

View full text Add to dashboard Cite

show abstract

“…Furthermore, some hybrid models represent input training data as the sum of two variables. For example, in [19], the NN and Autoregressive Moving Average (ARMA) models were combined to forecast hourly solar irradiation. They trained the ARMA with input training data, and then they trained the time delayed NN with residuals that are the difference between the input training data and the outputs from the ARMA.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

Baek

Lee

2017

Energies

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Total daily solar irradiation for the next day is forecasted through an ensemble of multiple machine learning algorithms using forecasted weather scenarios from numerical weather prediction (NWP) models. The weather scenarios were predicted at grid points whose longitudes and latitudes are integers, but the total daily solar irradiation was measured at non-integer grid points. Therefore, six interpolation functions are used to interpolate weather scenarios at non-integer grid points, and their performances are compared. Furthermore, when the total daily solar irradiation for the next day is forecasted, many data trimming techniques, such as outlier detection, input data clustering, input data pre-processing, and output data post-processing techniques, are developed and compared. Finally, various combinations of these ensemble techniques, different NWP scenarios, and machine learning algorithms are compared. The best model is to combine multiple forecasting machines through weighted averaging and to use all NWP scenarios.

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“…ARMA models have been used in diverse areas of applications such as speech [2], [3], seismology [4], video [5], image [6], etc. Particularly, they have been applied in energy and meteorological prediction studies of solar radiation [7], [8], electricity demand [9], [10] and wind speed [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model selection for PARMA processes using RJMCMC

Karakuş

Kuruoğlu

Altınkaya

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Many prediction studies using real life measurements such as wind speed, power, electricity load and rainfall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial ARMA (PARMA) models belong to the class of linear-in-the-parameters. In this paper, we propose a reversible jump Markov chain Monte Carlo (RJMCMC) based complete model estimation method which estimates PARMA models with all their parameters including the nonlinearity degree. The proposed method is unique in the manner of estimating the nonlinearity degree and all other model orders and model coefficients at the same time. Moreover, in this paper, RJMCMC has been examined in an anomalous way by performing transitions between linear and nonlinear model spaces.

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Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN

Cited by 274 publications

References 29 publications

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

Nonlinear model selection for PARMA processes using RJMCMC

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