Box–Jenkins multiplicative ARIMA modeling for prediction of solar radiation: a case study

Shadab, Ahzam; Said, Salim; Ahmad, Shamshad

doi:10.1007/s42108-019-00037-5

Cited by 36 publications

(17 citation statements)

References 44 publications

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“…Table 1 illustrates six selected models amongst a number of combinations that did agree to the data with parameter estimations on the basis of the statistics criterion (Martinez et al, 2011); (Hyndman and Athanasopoulos, 2018). Figure 7 shows autocorrelations and partial autocorrelations in terms of model residuals at different lags for the best six combinations selected on the basis of lowest AICc measures (Shadab et al, 2019). From Figure 7, it is evident that autocorrelation and partial autocorrelation at different lags for the residuals lie within 95% confidence level, thus implying that the autocorrelation coefficients are statistically insignificant.…”

Section: Model Estimationmentioning

confidence: 95%

Relative Humidity Forecasts in Tetouan (Morocco) with SARIMA model.

Ayad¹

2022

Journal of Earth Sciences and Geotechnical Engineering

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In this study, a seasonal ARIMA model is built using the Box and Jenkins method (1970) to predict the long-term relative humidity in the city of Tetouan. To this end, the monthly average relative humidity data over the period of 1990 to 2022 from the Sania Ramel station are used to build and verify the model. The methodology used in this work toward the development of the model includes five steps: exploratory analysis, model identification, parameter estimation, model validation and prediction. The validity of the model is tested by using the standardized residual plots along with the appropriate statistical tests given by Box and Jenkins. In a second validation step, the predicted values of monthly relative humidity are verified through the use of real data series. After carrying out the necessary checks, the ARIMA(2,1,1)(2,0,0)[12] model proved to be the most effective. Keywords: Time Series, Seasonal ARIMA, Relative Humidity, Forecast.

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Section: Model Estimationmentioning

confidence: 95%

Relative Humidity Forecasts in Tetouan (Morocco) with SARIMA model.

Ayad¹

2022

Journal of Earth Sciences and Geotechnical Engineering

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“…While physical models such as sky-image-based models explore the mechanism between solar radiation and other meteorological parameters 10 , empirical models are aimed at developing a linear or non-linear regression equation for solar radiation estimation 11 . Statistical models such as the autoregressive moving-average model (ARIMA), are developed based on statistical correlation 12 . In recent years, artificial intelligence (AI) models have been used for better solar radiation prediction.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals

Bamisile

Cai

Oluwasanmi

et al. 2022

Sci Rep

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Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.

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“…• Statistical models considering GHI measurements as time series. These are, for example, autoregressive moving average models (ARMA) (Voyant et al 2012), autoregressive integrated moving average models (ARIMA) (Shadab et al 2019), Kalman filter (Soubdhan et al 2016), and Markov chain (Vindel and Polo 2014) are commonly used statistical techniques. These approaches are generally used for reasonably short forecast horizons.…”

Section: Introductionmentioning

confidence: 99%

Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

Benmouiza

2022

Environ Sci Pollut Res

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The foundation for many solar energies uses as well as economic and environmental concerns is global solar irradiation information. However, due to solar irradiation and measurements variations, reliable worldwide statistics on solar irradiation are frequently impossible or difficult to acquire. In addition, more precise forecast of solar irradiation plays an increasingly important role in electric energy planning and management due to integrating photovoltaic solar systems into power networks. Hence, this paper proposes a new hybrid model for 1-hour ahead solar irradiation forecasting called LGC-GMDH (local gravitational clustering-Group method of data handling). The novel LGC-GMDH model is based on the local clustering that adequately captures the underlying features of the solar irradiation time series. Each cluster is then forecasted using the GMDH method, which is a self-organized system that is capable of handling very complicated nonlinear problems. Finally, these local forecasts are reconstructed in order to obtain the global forecast. Comparative study between the proposed model and the traditional individual models such as; backpropagation neural network (BP), supporting vector machines (SVM), long short-term memory (LTSM), hybrid models such; BP-MLP, RNN-MLP, LSTM-MLP hybrid wavelet packet decomposition (WPD), convolutional neural network (CNN) with LSTM-MLP, and ANFIS clustering shows that the proposed model overcomes conventional model deficiencies and achieves more precise predicting outcome.

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Box–Jenkins multiplicative ARIMA modeling for prediction of solar radiation: a case study

Cited by 36 publications

References 44 publications

Relative Humidity Forecasts in Tetouan (Morocco) with SARIMA model.

Relative Humidity Forecasts in Tetouan (Morocco) with SARIMA model.

Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals

Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

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