Statistical models approach for solar radiation prediction

Ferrari, S.; Lazzaroni, M.; Piuri, Vincenzo; Cristaldi, L.; Faifer, Marco

doi:10.1109/i2mtc.2013.6555712

Cited by 19 publications

(12 citation statements)

References 9 publications

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“…PV generation and household load form a time series with potentially recurrent patterns. The most prevalent methods for time series forecasting are auto-regressive (AR), Moving-Average (MA) and Autoregressive-Moving-Average (ARMA) models [16], which are used for PV generation [5,31,47] and load forecast [18,22,25]. However, they are generally not suitable to fully capture non-stationary processes like PV generation or household load.…”

Section: Forecastingmentioning

confidence: 99%

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Flexibility Disaggregation under Forecast Conditions

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Seidemann

Lechl

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Stationary battery energy storage systems and electric vehicles become more and more popular at households with local photovoltaic generation. Besides improving self-consumption and autarchy, these batteries can provide flexibility to an external utility. Thereby, generation and demand uncertainty, as well as cost optimality, need to be considered when utilizing distributed flexibility.This paper discusses long short-term memory neural networks for photovoltaic generation forecast and persistence models for household load forecast with respect to their applicability in local energy management system optimization. Furthermore, a mixedinteger linear program is proposed to optimally utilize local flexible loads and storage systems. Its solution space yields the flexibility potential, which can be aggregated at flexibility pools. In order to disaggregate flexibility requests to a pool of distributed energy management systems, we propose a heuristic algorithm that can among others minimize the overall flexibility cost or maximize probability of flexibility delivery. The forecast models, the mixed integer linear program and the flexibility disaggregation are evaluated on realistic household photovoltaic and load profiles to demonstrate the full chain from local forecast to flexibility disaggregation under forecast conditions. Our experiments with flexibility disaggregation show that the probability to provide flexibility should not be neglected when it comes to distributed energy management optimization based on forecast models. CCS CONCEPTS• Hardware → Smart grid; • Computing methodologies → Uncertainty quantification; Neural networks.

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Section: Forecastingmentioning

confidence: 99%

“…Equation ( 12) indicates whether to charge ( ( ) = 0) or discharge ( ( ) = 1) the ESS, and the variables in ( 13) and ( 14) define the charging and discharging power, respectively. To avoid charging and discharging at the same time, we add two additional constraints (15) and (16).…”

Section: Decision Variables and Constraintsmentioning

confidence: 99%

Flexibility Disaggregation under Forecast Conditions

Danner

Seidemann

Lechl

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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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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“…A commonly used avenue for time series prediction is the autoregressive moving average (ARMA) models after enesuring that the time series is stationary [19]. ARMA has the capability of extracting useful statistical properties using the well known Box and Jenkins model [20]. The general Autoregressive Moving Average, ARMA (p,q) for a solar radiation 𝑦 𝑡 , is given by:…”

Section: Arma Modelmentioning

confidence: 99%

A novel model for solar radiation prediction

et al. 2019

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Energy for fulfilling basic community/individual needs has come to constitute the first article of expectation in all contemporary societies. The exploitation of renewables notably solar in electricity generation has brought relief to the fulfilment of energy demand especially among susceptible communities. In this paper yearly minimum solar radiation of Kano (12.05°N; 08.2°E; altitude 472.5 m; 3 air density 1.1705 kg/m3) for 46 years is used to generate a prediction model that fits the data using autoregressive moving average (ARMA) and a new model termed autoregressive moving average process (ARMAP). Comparison between the ARMA and ARMAP models showed a tremendous improve in the sum of square error reduction between the actual data and the forecasted data by 47%.

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Statistical models approach for solar radiation prediction

Cited by 19 publications

References 9 publications

Flexibility Disaggregation under Forecast Conditions

Flexibility Disaggregation under Forecast Conditions

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

A novel model for solar radiation prediction

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