Deep learning algorithms for very short term solar irradiance forecasting: A survey

Ajith, Meenu; Martínez‐Ramón, Manel

doi:10.1016/j.rser.2023.113362

Cited by 30 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, the prediction research of the two has mainly been focused on smart grid and other applications [29,30]. A number of methods have been proposed to forecast solar irradiance, including persistence [31], classical statistical methods [32], machine learning [33], cloud motion tracking [34], numerical weather prediction [35], and hybrid models [36][37][38]. The hybrid prediction method is the most promising method for the solar irradiation prediction, which combines two or more methodologies to improve the advantages and accuracy while avoiding the drawbacks.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Trajectory Planning for Low-Altitude Solar-Powered Convertible UAV by Driven Modes

Cao,

Liu

2024

Drones

View full text Add to dashboard Cite

The conversion efficiency of solar energy and the capacity of energy storage batteries limit the development of low-altitude solar-powered aircrafts in the face of challenging meteorological phenomena in the lower atmosphere. In this paper, the energy planning problem of solar-power convertible unmanned aerial vehicles (SCUAVs) is studied, and a degressive state-of-charge (SOC) trajectory planning method with energy management strategy (EMS) is proposed. The SOC trajectory planning strategy is divided into four stages driven by three modes, which achieves the energy cycle of SCUAV’s long-endurance cruise and multiple hovers without the need to fully charge the battery SOC. The EMS is applied to control the output of solar cell/battery and power distribution for each stage according to three modes. A prediction model based on wavelet transform (WT), long short-term memory (LSTM) networks and autoregressive integrated moving average (ARIMA) is proposed for the weather forecast in the low altitude, where solar irradiance is used for the prediction of solar input power, and the wind and its inflow direction take into account the multi-mode power prediction. Numerical and simulation results indicate that the effectiveness of the proposed SOC trajectory planning method has a positive impact on low-altitude solar-powered aircrafts.

show abstract

Section: Introductionmentioning

confidence: 99%

State-of-Charge Trajectory Planning for Low-Altitude Solar-Powered Convertible UAV by Driven Modes

Cao,

Liu

2024

Drones

View full text Add to dashboard Cite

show abstract

“…The problem with this is the lack of a record of this data and its difficult accessibility. For this reason, a large number of studies concentrate on the prediction of irradiance [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Development of AI-Based Tools for Power Generation Prediction

Aravena-Cifuentes,

Nuñez-Gonzalez,

Elola

et al. 2023

Computation

View full text Add to dashboard Cite

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination, R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.

show abstract

“…Regarding the very short-term horizon, Ajith and Martínez-Ramón [7] compared three categories of solar irradiance forecasting methods: time series, sky camera images, and hybrid models combining infrared images with radiation time series. The authors showed that the normalized root mean square error (nRMSE) varied from 30 to 53% in terms of forecasting error.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera

David,

Alonso-Montesinos,

Le Gal La Salle

et al. 2023

Energies

View full text Add to dashboard Cite

With the fast increase of solar energy plants, a high-quality short-term forecast is required to smoothly integrate their production in the electricity grids. Usually, forecasting systems predict the future solar energy as a continuous variable. But for particular applications, such as concentrated solar plants with tracking devices, the operator needs to anticipate the achievement of a solar irradiance threshold to start or stop their system. In this case, binary forecasts are more relevant. Moreover, while most forecasting systems are deterministic, the probabilistic approach provides additional information about their inherent uncertainty that is essential for decision-making. The objective of this work is to propose a methodology to generate probabilistic solar forecasts as a binary event for very short-term horizons between 1 and 30 min. Among the various techniques developed to predict the solar potential for the next few minutes, sky imagery is one of the most promising. Therefore, we propose in this work to combine a state-of-the-art model based on a sky camera and a discrete choice model to predict the probability of an irradiance threshold suitable for plant operators. Two well-known parametric discrete choice models, logit and probit models, and a machine learning technique, random forest, were tested to post-process the deterministic forecast derived from sky images. All three models significantly improve the quality of the original deterministic forecast. However, random forest gives the best results and especially provides reliable probability predictions.

show abstract

Deep learning algorithms for very short term solar irradiance forecasting: A survey

Cited by 30 publications

References 49 publications

State-of-Charge Trajectory Planning for Low-Altitude Solar-Powered Convertible UAV by Driven Modes

State-of-Charge Trajectory Planning for Low-Altitude Solar-Powered Convertible UAV by Driven Modes

Development of AI-Based Tools for Power Generation Prediction

Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera

Contact Info

Product

Resources

About