Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units

Hosseini, Majid; Katragadda, Satya; Wojtkiewicz, Jessica; Gottumukkala, Raju; Maida, Anthony S.; Chambers, Terrence L.

doi:10.3390/en13153914

Cited by 21 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, to reduce the training time while ensuring high accuracy, the GRU has been applied for short term PV generation forecasts (Wang et al, 2018b). Similarly, multivariate GRU models (Wang et al, 2018b;Wojtkiewicz et al, 2019;Hosseini et al, 2020) have been proposed to forecast solar irradiance or power production. Note that further improvement of forecast accuracy requires the addition of exogenous weather variables and spatial cloud cover information, which could be extracted from sky imaging systems using convolutional neural networks (see Sections local-sensing methods and hybrid methods).…”

Section: Forecasts Based On Deep Learning Methodsmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

View full text Add to dashboard Cite

The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

show abstract

Section: Forecasts Based On Deep Learning Methodsmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

View full text Add to dashboard Cite

show abstract

“…Employing domain-reduction techniques can be beneficial to address these challenges. Hybrid solutions that use sky features as input to machine learning (ML) models are promising [9,[24][25][26]. These solutions do not use raw images but rather features characterizing sky conditions.…”

Section: Related Workmentioning

confidence: 99%

“…These solutions do not use raw images but rather features characterizing sky conditions. The simplest approach is to feed in the cloud cover besides other exogenous meteorological data to the model [25]. Cloud cover can be calculated using sky camera images [24] or obtained from another external source, i.e., online weather databases [25].…”

Section: Related Workmentioning

confidence: 99%

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Barancsuk,

Groma,

Günter

et al. 2024

Energies

View full text Add to dashboard Cite

In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.

show abstract

“…Internal memory in the Elman network that can deal with the variability of the PV data is considered as a direct result of this promising performance. Hosseini et al (2020) chose to utilize the recurrent networks, namely GRU and LSTM, to compare the univariate and multivariate approaches for direct normal irradiance hourly forecasting. They detected that computational-wise, GRU exhibited a better performance than the LSTM because LSTM is computationally time consuming with no significant superiority, especially for the multivariate approaches.…”

Section: Rnn For Solar Power Forecastingmentioning

confidence: 99%

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

et al. 2021

View full text Add to dashboard Cite

The global trend toward a green sustainable future encouraged the penetration of renewable energies into the electricity sector to satisfy various demands of the market. Successful and steady integrations of renewables into the microgrids necessitate building reliable, accurate wind and solar power forecasters adopting these renewables' stochastic behaviors. In a few reported literature studies, machine learning- (ML-) based forecasters have been widely utilized for wind power and solar power forecasting with promising and accurate results. The objective of this article is to provide a critical systematic review of existing wind power and solar power ML forecasters, namely artificial neural networks (ANNs), recurrent neural networks (RNNs), support vector machines (SVMs), and extreme learning machines (ELMs). In addition, special attention is paid to metaheuristics accompanied by these ML models. Detailed comparisons of the different ML methodologies and the metaheuristic techniques are performed. The significant drawn-out findings from the reviewed papers are also summarized based on the forecasting targets and horizons in tables. Finally, challenges and future directions for research on the ML solar and wind prediction methods are presented. This review can guide scientists and engineers in analyzing and selecting the appropriate prediction approaches based on the different circumstances and applications.

show abstract

Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units

Cited by 21 publications

References 31 publications

Intra-hour irradiance forecasting techniques for solar power integration: A review

Intra-hour irradiance forecasting techniques for solar power integration: A review

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

Contact Info

Product

Resources

About