Improved Probability Prediction Method Research for Photovoltaic Power Output

Cheng, Ze; Liu, Qi; Zhang, Wen

doi:10.3390/app9102043

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…For instance, Sheng et al [34] applied the GPR method to predict PV power. Cheng et al [9] proposed an improved sparse GPR model with an improved least square support vector machine (SVM) model for PV output power prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Kan

Lin

et al. 2021

Computational Intelligence and Neuroscience

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When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.

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Section: Literature Reviewmentioning

confidence: 99%

“…Consequently, their impact on the PV power generation forecast must be considered [8]. For the power forecast study, the PV system can be classified into ultrashort, short, medium, and long periods [9]. e ultra-short power forecast generally takes 0-4 hours, while the short power forecast takes 1-3 days.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Kan

Lin

et al. 2021

Computational Intelligence and Neuroscience

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“…Photovoltaic (PV) power generation is an efficient way to utilize solar energy, and the PV power generation proportion is increasing in line with reductions in cost and improvements in technology [5]. PV output power has fluctuation and uncertainty [6][7][8]; these characteristics bring difficulties to the optimal dispatching of power system and are not conductive to the stability of renewable energy power system [9][10][11]. The adverse impacts of PV power generation limit the improvement of the grid connection rate of PV power generation, and are not conducive to the application of clean energy.…”

Section: Introductionmentioning

confidence: 99%

An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Liu

Feng

et al. 2021

Symmetry

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Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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Using time-windowed solar radiation profiles to assess the daily uncertainty of solar thermal electricity production forecasts

Biencinto

González

Valenzuela

2022

Journal of Cleaner Production

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Improved Probability Prediction Method Research for Photovoltaic Power Output

Cited by 9 publications

References 30 publications

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Using time-windowed solar radiation profiles to assess the daily uncertainty of solar thermal electricity production forecasts

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