A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

Wang, Zhongju; Wang, Long; Huang, Chao; Luo, Xiong

doi:10.1109/tia.2022.3231842

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…In several studies, 15% of the power generation information was shared from nearby PV power plants [56,59,88], 6% of the studies used satellite images as the input source data [89,90], and some studies combined with sky images have been very promising. Such studies account for 5% of all studies, although further work is needed to correctly identify cloud layers [72,[91][92][93]. When considering their spatial resolution and the temporal level at which they are applied, NWP, satellite images, and sky images are plotted based on their spatial resolution, while the statistical methods are represented based on their spatial range.…”

Section: Distribution Of Input Data For the Reviewed Workmentioning

confidence: 99%

“…A forgetting mechanism or adaptive extreme learning machine is employed to optimize the number of neurons in the hidden layer within a certain range to solve the problem of the poor generalization ability of extreme learning machines [21,87]. Due to the advantages and disadvantages of different prediction models, hybrid prediction methods are used to optimize the data processing results of different models based on specific strategies to obtain better solar PV power generation prediction results and ultimately improve predictive accuracy [92,93]. It was found that hybrid prediction methods have the optimization characteristics of the prediction results.…”

Section: Statistical Metrics For the Reviewed Workmentioning

confidence: 99%

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A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation

Tsai

Hong

et al. 2023

Energies

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Accurately predicting the power produced during solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for its balanced operation and optimized dispatch and reduces operating costs. Solar PV power generation depends on the weather conditions, such as temperature, relative humidity, rainfall (precipitation), global solar radiation, wind speed, etc., and it is prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility, and intermittency. Recently, the demand for further investigation into the uncertainty of short-term solar PV power generation prediction and its effective use in many applications in renewable energy sources has increased. In order to improve the predictive accuracy of the output power of solar PV power generation and develop a precise predictive model, the authors used predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is an important aspect of optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods identified in the reviewed literature are classified according to the input data source, and the case studies and examples proposed are analyzed in detail. The contributions, advantages, and disadvantages of the predictive probabilistic methods are compared. Finally, future studies on short-term solar PV power forecasting are proposed.

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Section: Distribution Of Input Data For the Reviewed Workmentioning

confidence: 99%

Section: Statistical Metrics For the Reviewed Workmentioning

confidence: 99%

A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation

Tsai

Hong

et al. 2023

Energies

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“…The use of component classifiers learned from various groups to create a composite categorization system was suggested by [10] in order to improve the performance of identification systems. The popularity of ensemble learning has grown steadily in recent years [11][12][13][14][15][16][17]. Nowadays, ensemble learning is considered a key technique in the machine learning toolkit and is widely used in industry and the academic world.…”

Section: Introductionmentioning

confidence: 99%

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Majumder,

Gautam,

Basu

et al. 2024

PLoS ONE

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Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.

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“…In [31] and [32], an STLF in distribution networks is presented using a radial basis function neural network. In [33]- [35], deep learning methods are applied to perform STLF using Ensemble Extreme Learning Machines (ELM) and Knearest-neighbor (KNN). In [8] and [36], powerful ANNs including Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) are presented for load forecasting.…”

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confidence: 99%

Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter

ElMenshawy,

Massoud

2023

IEEE Access

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The intermittent non-dispatchable power produced by Renewable Energy Sources (RESs) in distribution networks caused additional challenges in load forecasting due to the introduced uncertainties. Therefore, high-quality load forecasting is essential for distribution network planning and operation. Most of the work presented in literature focusing on Short-Term Load Forecasting (STLF) has paid little consideration to the intrinsic uncertainty associated with the load dataset. A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. To avoid the divergence of conventional Kalman filter and improve the system stability, Adaptive Extended Kalman Filter (AEKF) is introduced through incorporating a moving-window method with the Extended Kalman Filter (EKF). Nonetheless, the moving window adds an extra computational burden. In this regard, this paper employs the concept of Forgetting Factor AEKF (FFAEKF) for STLF in distribution networks to avoid the computational burden introduced by the AEKF. The forgetting factor improves the estimation accuracy and increases the system convergence when compared with the AEKF. In this paper, the AEKF and FFAEKF are compared in terms of their performance using Maximum Absolute Error (MaxAE) and Root Mean Square Error (RMSE). Matlab/Simulink platform is used to apply the AEKF and FFAEKF algorithms on the load dataset. Results have demonstrated that the FFAEKF improves the forecasting performance through providing less MaxAE and less RMSE. In which, the FFAEKF MaxAE and RMSE are reduced by two and three times, respectively, compared to the AEKF MaxAE and RMSE.

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A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

Cited by 9 publications

References 29 publications

A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation

A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter

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