Time Dependent Classification of Solar Radiation Sequences Using Best Information Criterion

Soubdhan, Ted; Abadi, Mohamed; Emilion, Richard

doi:10.1016/j.egypro.2014.10.121

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…A classification of daily irradiance profiles was performed. Often, in the literature, in solar area, the k-means method is used [13,27,36]. Recently, the Fuzzy C-means clustering is used for the implementation of demand side management measures [42] or to classify global horizontal irradiance [28].…”

Section: Methods Of Insolation Conditions Classificationmentioning

confidence: 99%

“…This parameter removes the effect of daily solar trend and normalizes variability to unity. The clearness index K t is usually employed for solar radiation clustering [25,27,36,37]. Values of K t are bounded between 0 and 1.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…In this paper, for the first time in the literature, we present the forecasting performance of a model as a function of two horizon strategies on the modeling process and as a function of daily clearness index classification. For the solar radiation classification, several methods are proposed in the literature based on unsupervised clustering methods such as k-means [13,25], Ward's method [26], best information criterion [27] or, recently, Fuzzy c-means method [28]. Fuzzy c-means method was used here.…”

Section: Introductionmentioning

confidence: 99%

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Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

et al. 2019

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The tropical insular region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting. Therefore, it is necessary to develop and use performant and robustness forecasting techniques. This paper examines the predictive performance of a novel solar forecasting approach, the multiscale hybrid forecast model (MHFM), as a function of several parameters. The MHFM model is a technique recently used for irradiance forecasting based on a hybrid autoregressive (AR) and neural network (NN) model combined with multiscale decomposition methods. This technique presents a relevant performance for 1 h ahead global horizontal irradiance forecast. The goal of this work is to highlight the strength and limits of this model by assessing the influence of different parameters from a metric error analysis. This study illustrates modeling process performance as a function of daily insolation conditions and testifies the influence of learning data and test data time scales. Several forecast horizon strategies and their influence on the MHFM performance were investigated. With the best strategy, a rRMSE value from 4.43 % to 10.24 % was obtained for forecast horizons from 5 min to 6 h. The analysis of intra-day solar resource variability showed that the best performance of MHFM was obtained for clear sky days with a rRMSE of 2.91 % and worst for cloudy sky days with a rRMSE of 6.73 % . These works constitute an additional analysis in agreement with the literature about influence of daily insolation conditions and horizons time scales on modeling process.

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Section: Methods Of Insolation Conditions Classificationmentioning

confidence: 99%

Section: Data Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

et al. 2019

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“…Time series has been defined as a set of numbers that measures the status of some activity over time. It is the historical record of some activity, with measurements taken at equally spaced intervals with a consistency in the activity and the method of measurement [8]. Autoregressive Integrated Moving Average (ARIMA) model has been studied extensively to time series analysis and forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Comparative study of Time Series, Empirical Orthogonal Transformation and Descriptive Statistical Analysis on Meteorological Parameters over Ogoja and Maiduguri

Akpootu

Tijjani

Gana

2019

JENRR

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In this study, time series statistical analysis was carried out on the monthly average daily meteorological parameters of global solar radiation, sunshine hours, wind speed, mean temperature, rainfall, cloud cover and relative humidity during the period of thirty one years (1980 – 2010) using IBM SPSS Statistics version 20 with expert modeler to determine the level, trend and seasonal variations for Ogoja and Maiduguri. Seasonal Auto Regressive Integrated Moving Average models were determined for the two locations along with their respective statistical indicators of coefficient of determination, Root Mean Square Error, Mean Absolute Percentage Error and Mean Absolute Error and are found suitable for one step ahead forecast for the studied area. The factor analysis (empirical orthogonal transformation) and descriptive statistical analysis was also carried out for the study areas under investigation. The results indicated that the model type for all the meteorological parameters for Ogoja is simple seasonal while that for Maiduguri is simple seasonal except for rainfall and cloud cover with winter’s additive and ARIMA models respectively. The correlation matrix obtained from the factor analysis for the studied area indicated that the global solar radiation and wind speed are more correlated with the mean temperature. The sunshine hours and mean temperature are more correlated with the global solar radiation. The rainfall is more correlated with the relative humidity; similarly, the relative humidity is more correlated with the rainfall. However, the cloud cover is more correlated to the rainfall for Ogoja while for Maiduguri the cloud cover is more correlated to the relative humidity. The component matrix analysis revealed that two seasons are identified for Ogoja; the rainy and dry seasons while for Maiduguri three seasons are identified; the rainy, cool dry (harmattan) and hot dry seasons. The skewness and kurtosis test for Ogoja indicated that the global solar radiation, sunshine hours, cloud cover and relative humidity are negatively skewed and the wind speed, mean temperature and rainfall are positively skewed while the global solar radiation, sunshine hours, wind speed, cloud cover and relative humidity indicates possibility of a leptokurtic distribution and the mean temperature and rainfall indicates possibility of a platykurtic distribution. The skewness and kurtosis for Maiduguri indicated that the solar radiation, rainfall and relative humidity are positively skewed and the sunshine hours, wind speed, mean temperature and cloud cover are negatively skewed while the global solar radiation, rainfall and cloud cover indicates possibility of a leptokurtic distribution and the sunshine hours, wind speed, mean temperature and relative humidity indicates possibility of a platykurtic distribution.

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“…TS is a set of numbers that measures the status of some activity over time. It is the historical record of some activity, with measurements taken at equally spaced intervals with a consistency in the activity and the method of measurement [6]. Some of the best predictors found in literature are Autoregressive and moving average (ARMA) [5,7,8], Bayesian inferences [9,10], Markov chains [11], k-Nearest-Neighbors predictors [12] or artificial intelligence techniques as the Artificial Neural Network (ANN) [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

Voyant

Soubdhan

Lauret

et al. 2015

Energy

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In this paper we propose to determinate and to test a set ofstatistical parameters (20)to estimate the predictability of the global horizontal irradiation time series and thereby propose a new prospective tool indicating the expected error regardlessthe forecasting methodsa modeller can possibly implement. The mean absolute log return, which is a tool usually used in econometry, proves to be a very good estimator. Some examples of the use of this tool are exposed, showing the interest of this statistical parameter in concrete cases of predictions or optimizations.

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Time Dependent Classification of Solar Radiation Sequences Using Best Information Criterion

Cited by 8 publications

References 11 publications

Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

A Comparative study of Time Series, Empirical Orthogonal Transformation and Descriptive Statistical Analysis on Meteorological Parameters over Ogoja and Maiduguri

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

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