Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models

Çolak, İlhami; Yeşilbudak, Mehmet; Genç, Naci; Bayındır, Ramazan

doi:10.1109/icmla.2015.33

Cited by 77 publications

(34 citation statements)

References 27 publications

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“…Gross & Galiana (1987) also successfully used the ARIMA model to forecast short-term loads. In fact, the superior performance of the ARIMA model over other linear time-series models, such as AR, MA, ARMA, has been documented in many energy forecasting studies (Valipour, Banihabib, & Behbahani, 2013;Marriott & Newbold, 1998;Chujai & Kerdprasop, 2013;Colak, Yesilbudak, Genc, & Bayindir, 2015;Wang, Hsu, & Liou, 2011). Additionally, the ARIMA model was found to have fewer residual errors than the ARMA model (Valipour et al, 2013) and less bias than either MA or AR model (Wang, Hsu, & Liou, 2011).…”

Section: Previous Studiesmentioning

confidence: 89%

The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

Qin¹,

Shanks²,

Phillips³

et al. 2019

IRHE

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The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

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Section: Previous Studiesmentioning

confidence: 89%

The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

Qin¹,

Shanks²,

Phillips³

et al. 2019

IRHE

View full text Add to dashboard Cite

show abstract

“…A detailed description of the ARIMA model can be found elsewhere and further applications of this method can be found in other's works [31,94,95]. Generally, the ARIMA model assumes a scenario where there is no change in consecutive periodical measurements or the readings used to construct a model.…”

Section: Computational Aspects Of Lstm Network Modelmentioning

confidence: 99%

“…The construction of a solar radiation-forecasting model in general and the global solar radiation (GSR) model, in particular, have been intensively explored. With the recent advances of computational data science, machine learning-based forecasting models typically provide distinct advantages over physical models [17,19,20] and time-series models [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Models based on machine learning and neural networks have evolved over recent decades.…”

Section: Introductionmentioning

confidence: 99%

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

et al. 2020

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This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

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“…ARIMA is regarded as a smooth technique, and it is applicable when the data is reasonably long and the correlation between past observations is stable [22]. Several studies in the literature have used ARMA and ARIMA models for solar radiation prediction [23][24][25][26]. The ARMA and ARIMA models have also been compared in terms of the goodness-of-fit values produced by the log-likelihood function.…”

Section: Literature Reviewmentioning

confidence: 99%

Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

2019

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Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.

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Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models

Cited by 77 publications

References 27 publications

The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

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