Classification and summarization of solar irradiance and power forecasting methods: A thorough review

Yang, Bo; Zhu, Ting; Cao, Ping; Guo, Zhengxun; Zeng, Chunyuan; Li, Danyang; Chen, Yijun; Ye, Haoyin; Shao, Ruining; Shu, Hongchun; Yu, Tao

doi:10.17775/cseejpes.2020.04930

Cited by 26 publications

(21 citation statements)

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“…In general, PV data have time-series measurements [ 99 ]. In post-processing as a cooperative ensemble forecasting method, the forecasting will be performed using the time series consecutively by two or more predictors.…”

Section: Ensemble Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

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Section: Ensemble Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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“…Forecasting Target Year [25] Ability to pinpoint seven crucial perspectives and trends for prospective investigations in solar forecasting. These findings are designed to help readers better utilize these approaches for more profound future research.…”

Section: Ref Descriptionmentioning

confidence: 99%

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Tercha,

Tadjer,

Chekired

et al. 2024

Energies

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The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

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“…The first two layers present indirect connections that create an associative memory. On the other hand, the lower layers present direct connections [62].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

et al. 2022

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Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics.

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Classification and summarization of solar irradiance and power forecasting methods: A thorough review

Cited by 26 publications

References 0 publications

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

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