A comprehensive review of hybrid models for solar radiation forecasting

Guermoui, Mawloud; Melgani, Farid; Gairaa, Kacem; Mekhalfi, Mohamed Lamine

doi:10.1016/j.jclepro.2020.120357

Cited by 186 publications

(71 citation statements)

References 96 publications

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“…Increasingly, powerful artificial intelligence techniques are being developed that should lead to substantial progress. Finally, an optimized combination (or hybridization) of different physical and statistical models will be an advantage for any solar power prediction system (Guermoui et al 2020;.…”

Section: Summary Of Applications Of Solar Resource Datamentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition

Sengupta

Habte

Wilbert

et al. 2021

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Section: Summary Of Applications Of Solar Resource Datamentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition

Sengupta

Habte

Wilbert

et al. 2021

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“…Several studies have indicated that the hybrid approach has shown better performance than the single method Several studies have indicated that the hybrid approach has shown better performance than the single method [48][49][50][51]. A comprehensive review on hybrid models for solar energy forecasting is presented in [10]. In this study, k-means clustering is used for weather classification and LSTM-RNN for forecasting the SI.…”

Section: Related Workmentioning

confidence: 99%

“…A highly accurate SI forecasting leads to a secure and optimized solution for the power system [9]. In addition, SI forecasting has been performed for various operational and management applications such as scheduling, reserve management, congestion management, and so on [10]. These applications are associated with specific forecasting time scales and horizons.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

Zafar

Husein

et al. 2021

Applied Sciences

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At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. Day-ahead solar irradiance (SI) forecasting has various applications for system operators and market agents such as unit commitment, reserve management, and biding in the day-ahead market. To this end, a hybrid recurrent neural network is presented herein that uses the long short-term memory recurrent neural network (LSTM-RNN) approach to forecast day-ahead SI. In this approach, k-means clustering is first used to classify each day as either sunny or cloudy. Then, LSTM-RNN is used to learn the uncertainty and variability for each type of cluster separately to predict the SI with better accuracy. The exogenous features such as the dry-bulb temperature, dew point temperature, and relative humidity are used to train the models. Results show that the proposed hybrid model has performed better than a feed-forward neural network (FFNN), a support vector machine (SVM), a conventional LSTM-RNN, and a persistence model.

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“…These methods are mainly formed to overcome the shortcomings of each method and improve the forecast accuracy. Some of such hybrid methods proposed by researchers [119][120][121][122] have outperformed the individual forecasting (see [123] for a detailed review on hybrid models). A hybrid model was developed by [119,121], which was used to forecast total daily solar irradiance sequences.…”

Section: Hybrid Modelsmentioning

confidence: 99%

Solar irradiance resource and forecasting: a comprehensive review

Kumar

Yagli

Kashyap³

et al. 2020

IET Renewable Power Generation

109

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With the increase in demand for energy, penetration of alternative sources of energy in the power grid has increased. Photovoltaic (PV) energy is the most common and popular form of energy sources which is widely integrated into the existing grid. As solar energy is intermittent in nature, to ensure uninterrupted and reliable power supply to the prosumers, it is essential to forecast the solar irradiance. Accurate solar forecasting is necessary to facilitate large-scale modelling and deployment of PV plants without disrupting the quality and reliability of the power grid as well as to manage the power demand and supply. There are various methods to predict the solar irradiance such as numerical weather prediction methods, satellite-based approaches, cloud-image based methodologies, data-driven methods, and sensor-network based approaches. This study gives an overall review of the different resources and methods used for forecasting solar irradiance in different time horizons and also gives an extensive review of the sensor networks that are used for determining solar irradiance. The various error metrics and accessible data sets available for the sensor networks are also discussed that can be used for validation purposes.

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A comprehensive review of hybrid models for solar radiation forecasting

Cited by 186 publications

References 96 publications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition

Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

Solar irradiance resource and forecasting: a comprehensive review

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