The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu, Dandan; Shao, Haijian; Deng, Xing; Wang, Xia

doi:10.32604/cmes.2022.019245

Cited by 3 publications

(3 citation statements)

References 183 publications

(177 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study conducted by Xu et al [18], the current state of research on renewable energy generation and predictive technology for wind and photovoltaic energy was described. The authors proposed a short-term forecast model for multivariable wind energy using the LSTM sequential structure with an optimized hidden layer topology.…”

Section: Related Workmentioning

confidence: 99%

“…The results of the tests demonstrate that using solely layers with recurrent neurons does not yield superior performance when compared to incorporating an additional layer of shallow neurons. Although primary studies in the literature have reviewed explored hybrid models, they have not specifically combined RNN layers with ANN layers, as is done in this study [14,17,18,24,27,37].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

2023

View full text Add to dashboard Cite

In this article, forecast models based on a hybrid architecture that combines recurrent neural networks and shallow neural networks are presented. Two types of models were developed to make predictions. The first type consisted of six models that used records of exported active energy and meteorological variables as inputs. The second type consisted of eight models that used meteorological variables. Different metrics were applied to assess the performance of these models. The best model of each type was selected. Finally, a comparison of the performance between the selected models of both types was presented. The models were validated using real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type had a root mean square error (RMSE) of 0.19, a mean square error (MSE) of 0.03, a mean absolute error (MAE) of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower accuracy in the metrics: RMSE = 0.24, MSE = 0.06, MAE = 0.10, correlation coefficient = 0.95, and determination coefficient = 0.90. Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy generation of the solar plant.

show abstract

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

2023

View full text Add to dashboard Cite

show abstract

“…Similarly, VMD and SVM were combined in [157] to obtain feature vectors. Then, SFS, SBS, and Gram-Schmidt Orthogonalization were applied to eliminate redundant features, and SVM was employed to improve the anti-interference ability, speed, and accuracy [158,159].…”

Section: Support Vector Machinementioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

show abstract

Photovoltaic Energy Forecast Using Weather Data Through a Hybrid Model of Recurrent and Shallow Neural Networks

Castillo-Rojas¹,

Quispe²,

Hernández³

2023

Preprint

View full text Add to dashboard Cite

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

show abstract

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Cited by 3 publications

References 183 publications

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Photovoltaic Energy Forecast Using Weather Data Through a Hybrid Model of Recurrent and Shallow Neural Networks

Contact Info

Product

Resources

About