Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

Nespoli, Alfredo; Niccolai, Alessandro; Ogliari, Emanuele; Perego, Giovanni; Collino, Elena; Ronzio, Dario

doi:10.1016/j.apenergy.2021.117834

Cited by 52 publications

(12 citation statements)

References 38 publications

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“…In forecasting practice, different forecasting methods are typically used for the same problem. According to system theory, the object of prediction can be a complex social system or economic system, composed of interrelated and mutually restrictive elements [3]. Generally, a single prediction model can only provide corresponding effective information for prediction from a certain angle, while ignoring the effective information provided by other angles.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial customer classification by combined model

Liu

Zheng

2022

Applied Mathematics and Nonlinear Sciences

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This paper explores the pros and cons of different algorithm models on the same selection problem, and then uses the combined prediction theory to obtain a new combined prediction model to explore its prediction accuracy. The actual problem to be solved is to help financial institutions to scientifically classify customers who choose financial products. We select the bank data set in the UCI database, which is derived from the survey data of a customer conducted by a financial institution in Portugal for a wealth management product. Decision tree C5.0 algorithm, naive Bayes classification algorithm and binary logit model are individually used to carry out a single model of empirical research on financial product customer classification. Through the empirical analysis of the five combination models, it is concluded that in the model that uses the least squares weighting method to determine the weight, the weight appears negative, which does not conform to the actual situation. The model that is based on the least squares weighting method and the model that is based on the simple weighting method are excluded. In contrast, the arithmetic mean weighted model is better than the reciprocal variance weighted model and the reciprocal mean square model. The accuracy reaches 89.91%, which is 0.43% higher than the accuracy of a single model. It can be concluded that the model that is based on the arithmetic average weighting is a better combination forecasting model.

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Section: Literature Reviewmentioning

confidence: 99%

Financial customer classification by combined model

Liu

Zheng

2022

Applied Mathematics and Nonlinear Sciences

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“…Sky cameras and satellite images have been used for Nowcasting solar radiation (4) . Satellite images have been used to forecast global horizontal solar irradiance and are compared with the persistence model (5,6) . Nespoli & Niccolai (7) have used the sky image captured via a high-resolution camera installed on-site for the Now-casting of PV output.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting of Weather Parameters Impacting Solar PV Output Using Grey System Model

Uma¹,

Sridhar²,

Sumathi³

2023

IJST

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Objectives: To nowcast the weather parameters having a direct impact on the power output of the solar PV installations, with high prediction accuracy and a limited quantity of past data. Methods: In this study, the GM (1,1) model with Fourier series of error residuals has been proposed and used for forecasting the weather parameters namely Ambient temperature, Solar Photo Voltaic Module temperature, and Solar Irradiation. Real-time data has been used for showing the suitability of the proposed model to nowcast the weather parameters. The existing models like Autoregression and Double Exponential Smoothing are applied to the same data to prove the superiority of the GM (1,1) model with Fourier series of error residuals. Findings: It is found that the GM (1,1) model with Fourier series of error residuals is an apt model for nowcasting the weather parameters. The accuracy of the predicted result of this model on the real-time data ascertains the appropriateness of this model for nowcasting. The precision of the prediction accuracy of GM (1,1) with the Fourier series of error residuals model is verified by comparing it with other time series prediction models such as Autoregression and Double Exponential Smoothing algorithms. Novelty: Using GM (1,1) with the Fourier series of error residuals model for nowcasting the weather data is novel when compared with the existing algorithms because for nowcasting the weather parameters with accuracy, many of the existing algorithms require a huge volume of past data and involve complex computation. On the other hand, GM (1,1) with the Fourier series of error residuals model requires only a limited measure of past data and involves simple computation. Moreover, the accuracy of prediction is significantly higher than the other models.

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“…It is exploited to validate climate models [2] and to improve Numeric Weather Prediction (NWP) models [3]; for instance, it improves the definition of the cloud-drift vector field, allowing a more effective modeling of the atmosphere dynamics [4]. Moreover, CBH is also useful in the nowcasting (very short-term forecasting) of solar resources [5] and photovoltaic power plant energy outputs [6]. As clouds are the primary cause of intermittency in solar irradiance, they are of interest for solar power applications: an accurate calculation of the ground shadowing requires the knowledge of cloud height and extent [7].…”

Section: Introductionmentioning

confidence: 99%