Short term cloud nowcasting for a solar power plant based on irradiance historical data

Caballero, Rafael; Zarzalejo, Luis F.; Otero, Álvaro; Piñuel, Luis; Wilbert, Stefan

doi:10.24215/16666038.18.e21

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Additionally, in this kind of time series, it is recommended to limit the dataset according to some specific hours in which the variables to be predicted take a significant value. As an example, for solar radiation, it is common to restrict data to diurnal hours [26,30]. In this case, only nocturnal hours were considered, from 23 p.m. to 7 a.m. the next day.…”

Section: Methodsmentioning

confidence: 99%

“…Other research presented a short-term cloud forecast based on irradiance historical data collected in a radiometric network in Almeria (Spain) [30]. When applying a recurrent neural network known as Long Short-Term Memory (LSTM), they concluded that this method shows improvement in comparison with other common methods used for time series analysis.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Astrotourism and Night Sky Brightness Forecast: First Probabilistic Model Approach

C-Sánchez

Medina

Alonso-Hernández

et al. 2019

Sensors

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Celestial tourism, also known as astrotourism, astronomical tourism or, less frequently, star tourism, refers to people’s interest in visiting places where celestial phenomena can be clearly observed. Stars, skygazing, meteor showers or comets, among other phenomena, arouse people’s interest, however, good night sky conditions are required to observe such phenomena. From an environmental point of view, several organisations have surfaced in defence of the protection of dark night skies against light pollution, while from an economic point of view; the idea also opens new possibilities for development in associated areas. The quality of dark skies for celestial tourism can be measured by night sky brightness (NSB), which is used to quantify the visual perception of the sky, including several light sources at a specific point on earth. The aim of this research is to model the nocturnal sky brightness by training and testing a probabilistic model using real NSB data. ARIMA and artificial neural network models have been applied to open NSB data provided by the Globe at Night international programme, with the results of this first model approach being promising and opening up new possibilities for astrotourism. To the best of the authors’ knowledge, probabilistic models have not been applied to NSB forecasting.

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Section: Methodsmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

Astrotourism and Night Sky Brightness Forecast: First Probabilistic Model Approach

C-Sánchez

Medina

Alonso-Hernández

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, due to its ability of removing abundant information to resolve vanishing gradient issues, LSTM is appropriate to represent the learning data over different temporal domains [17]. Hence, LSTM has been studied in solar forecasting during the past five years [15,17,44,[53][54][55][56][57][58][59][60][61][62][63]. For instance, the first study regarding LSTM [64] demonstrated its forecasting skills for one-day ahead utilizing remote-sensing data under various topographical conditions with the best root mean square error (RMSE)~24% and mean absolute error (MAE)~17%.…”

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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Designing a long short-term network for short-term forecasting of global horizontal irradiance

et al. 2021

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Long short-term memory (LSTM) models based on specialized deep neural network-based architecture have emerged as an important model for forecasting time-series. However, the literature does not provide clear guidelines for design choices, which affect forecasting performance. Such choices include the need for pre-processing techniques such as deseasonalization, ordering of the input data, network size, batch size, and forecasting horizon. We detail this in the context of short-term forecasting of global horizontal irradiance, an accepted proxy for solar energy. Particularly, short-term forecasting is critical because the cloud conditions change at a sub-hourly having large impacts on incident solar radiation. We conduct an empirical investigation based on data from three solar stations from two climatic zones of India over two seasons. From an application perspective, it may be noted that despite the thrust given to solar energy generation in India, the literature contains few instances of robust studies across climatic zones and seasons. The model thus obtained subsequently outperformed three recent benchmark methods based on random forest, recurrent neural network, and LSTM, respectively, in terms of forecasting accuracy. Our findings underscore the importance of considering the temporal order of the data, lack of any discernible benefit from data pre-processing, the effect of making the LSTM model stateful. It is also found that the number of nodes in an LSTM network, as well as batch size, is influenced by the variability of the input data.

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Short term cloud nowcasting for a solar power plant based on irradiance historical data

Cited by 3 publications

References 12 publications

Astrotourism and Night Sky Brightness Forecast: First Probabilistic Model Approach

Astrotourism and Night Sky Brightness Forecast: First Probabilistic Model Approach

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

Designing a long short-term network for short-term forecasting of global horizontal irradiance

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