Performance Analysis of Neural Network Training Algorithms and Support Vector Machine for Power Generation Forecast of Photovoltaic Panel

Silva, Thiago Vieira da; Monteiro, Raul Vitor Arantes; Moura, Fabrício Augusto Matheus; Albertini, Madeleine R. M. C.; Tamashiro, Márcio Augusto; Guimarães, Geraldo Caixeta

doi:10.1109/tla.2017.7932697

Cited by 16 publications

(4 citation statements)

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“…For power generation forecasting of photovoltaic panels, da Silva et al [59] used ANN with seven training algorithms, which outperformed SVM. Leva et al [60] proposed ANN for PV plant energy forecasting to analyze the input dataset sensitivity.…”

Section: Neural Network-related Approachesmentioning

confidence: 99%

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Deng¹,

Da²,

Shao³

et al. 2023

Energy Engineering

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Photovoltaic power generating is one of the primary methods of utilizing solar energy resources, with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy. In order to provide reference strategies for pertinent researchers as well as potential implementation, this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches, statistical approaches and optimization techniques for solar power generation and forecasting. Deep learning-related methods, in particular, can theoretically handle arbitrary nonlinear transformations through proper model structural design, such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting. The research's results indicate that RBFNN-AG performed the best when applying the predetermined number of days, with an NRMSE value of 4.65%. RBFNN-AG performs better than sophisticated models like DenseNet (5.69%), SLFN-ELM (5.95%), and ANN-k-means-linear regression correction (6.11%). Additionally, scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.

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Section: Neural Network-related Approachesmentioning

confidence: 99%

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Deng¹,

Da²,

Shao³

et al. 2023

Energy Engineering

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“…An auto-encoder version attempts to decrease the enter data p via way of means of the usage of a supervised mastering procedure, in which the distinction among authentic enter p and reconstructed enter p ′ . For the learning of an auto-encoder, backpropagation is appropriate [25]. The learning method of an auto-encoder is specifically primarily based totally on backpropagation through the use of 3 steps.…”

Section: Decodermentioning

confidence: 99%

Recognition of cancer mediating genes using the novel MLP-SDAE model

Sheet

Ghosh

et al. 2022

Preprint

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In the present article, we introduce a deep auto-encoder model to have identified some possible genes mediating different cancer. This new version is a regression-primarily based totally fateful version primarily based totally on MLP and Stacked Denoising Auto-Encoder and the model name is MLP-SDAE. This version is educated via way of means of the usage of a Stacked Denoising Auto-Encoder for characteristic selection. Also, there’s a MLP structure for backpropagation. We have also included dropout to prevent over-fitting to improve our proposed model as well as performance. The process entails companies of gene-primarily based totally correlation coefficients and in the end decided on a few feasible genes. A specified comparative examine has been executed with a number of the prevailing deep studying procedures, such as a Recurrent Neural Network (RNN), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Auto-encoder (AE), and Denoising Auto-encoder (DAE). Four microarray gene expression data sets like human leukemia, lung, colon, and breast cancer are 1 Springer Nature 2021 L A T E X template Recognition of cancer mediating genes using the novel MLP-SDAE model successfully applied in this model. The results are verified using the preliminary test, gene expression profile plots, biochemical pathway, t-test, and also identified GO type of the important genes based on p-values.

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“…ese references perform time series forecasting to predict weather and then obtain the short-term PV forecast power. Some other references on time series forecasting focus on different algorithms, e.g., traditional physical model prediction [13], BP-artificial neutral network (ANN) prediction with accurate numeric weather forecast [14], extreme learning machine (ELM) [15], and support vector machine (SVM) [16,17]. In [18], a new model combines two well-known methods: the seasonal auto-regressive integrated moving average method and support vector machines method are proposed for short-term power forecasting of a grid-connected photovoltaic plant.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

Jia

Hao

et al. 2021

Mathematical Problems in Engineering

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With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

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Performance Analysis of Neural Network Training Algorithms and Support Vector Machine for Power Generation Forecast of Photovoltaic Panel

Cited by 16 publications

References 0 publications

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Recognition of cancer mediating genes using the novel MLP-SDAE model

Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

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