Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Ahn, Hyungkeun; Park, Neungsoo

doi:10.3390/en14020436

Cited by 58 publications

(34 citation statements)

References 28 publications

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“…Photovoltaic power prediction technology is a much-needed in-depth study, and accurate prediction of photovoltaic power generation has a better impact on the optimal scheduling of the power grid and power quality. It can be seen that the selection of similar daily data is very important and is an important topic for our future research [ 14 ]. Photovoltaic power generation systems include both stand-alone solar power generation systems and grid-connected solar power generation systems.…”

Section: Short-term Power Forecasting Methods Of Photovoltaic Power G...mentioning

confidence: 99%

Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics

Zhang¹,

Zhao²,

Kang

et al. 2022

Computational Intelligence and Neuroscience

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Along with the increasing prominence of energy and environmental issues, solar energy has received more and more extensive attention from countries around the world, and the installed capacity of photovoltaic power generation, as one of the main forms of solar energy development, has developed rapidly. Solar energy is by far the largest available source of energy on Earth, the use of solar power photovoltaic system has the advantages of flexible installation, simple maintenance, environmentally friendly, etc., by the world’s attention, especially the grid-connected photovoltaic power generation system has been rapid development. However, photovoltaic power generation itself is intermittent, affected by irradiance and other meteorological factors very drastically, and its own randomness and uncertainty are very large, and its grid connection affects the stability of the entire power grid. Therefore, the short-term prediction of photovoltaic power generation has important practical significance and guiding meaning. Multi-input deep convolutional neural networks belong to deep learning architectures, which use local connectivity, weight sharing, and subpolling operations, making it possible to reduce the number of weight parameters that need to be trained so that convolutional neural networks can perform well even with a large number of layers. In this paper, we propose a multi-input deep convolutional neural network model for PV short-term power prediction, which provides a short-term accurate prediction of PV power system output power, which is beneficial for the power system dispatching department to coordinate the cooperation between conventional power sources and PV power generation and reasonably adjust the dispatching plan, thus effectively mitigating the adverse effects of PV power system access on the power grid. Therefore, the accurate and reasonable prediction of PV power generation power is of great significance for the safe dispatch of power grid, maintaining the stable operation of power grid, and improving the utilization rate of PV power plants.

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Section: Short-term Power Forecasting Methods Of Photovoltaic Power G...mentioning

confidence: 99%

Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics

Zhang¹,

Zhao²,

Kang

et al. 2022

Computational Intelligence and Neuroscience

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“…Their R2 scores are 0.963 and 0.927, respectively. In these tests, the proposed deep RNN-based relatively brief prediction method outperformed the competition in terms of classification accuracy [9].…”

Section: Related Workmentioning

confidence: 97%

A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

Ramkumar

Sahoo

Amirthalakshmi³

et al. 2021

International Journal of Photoenergy

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Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.

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“…In this research, the architecture used for dynamic mapping was a Layer Recurrent Neural Network (LRNN), which is a shallow type with a recurrent inner connection and correlated with a tap delay; this feature allows the usage of previous states and present inputs to produce outputs within hidden states [83]. These ANNs kinds were proved to be efficient for modeling and mapping hysteresis phenomenon [84].…”

Section: Neural Network Compensation Detailedmentioning

confidence: 99%

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

et al. 2021

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Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

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Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Cited by 58 publications

References 28 publications

Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics

Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics

A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

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