Research of Photovoltaic Power Forecasting Based on Big Data and mRMR Feature Reduction

Liu, Juhua; Sun, Hao; Chang, Peng; Jiao, Zaibin; Ping, Wei; Ke, Xianbo; Sun, Xiaoyun; Cheng, Lin

doi:10.1109/pesgm.2018.8585967

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Bayesian optimization in [29] was applied to select the optimal combined features, and a deep learning model was constructed based on a LSTM block and an embedding block with the connection of a merge layer in order to predict the day-ahead PV power output. A support vector regression (SVR) method was proposed in one-hour-ahead PV power prediction [30]. Big data and minimum redundancy maximum relevance (mRMR) technology were applied to input features dimension reduction, while GA was used to optimize the learning parameters inside SVR machine; thus, the computational speed and the forecasting precision were both increased.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Chen

Zhu

2020

Electronics

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With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

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Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Chen

Zhu

2020

Electronics

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“…However, this lacks the influence of irradiance intensity, temperature and prediction foresight period on prediction results. Kim, G G. et al [29] considered the effects of irradiance intensity, ambient temperature, wind speed, relative humidity and other factors on the maximum photovoltaic output, but ignored the random and intermittent nature of photovoltaic output; Liu, J. et al [30] used maximum relevance minimum redundancy (mRMR) to reduce the dimension of the characteristic factor, so as to quantitatively analyze the relationship between the meteorological parameter and the photovoltaic output, but only the original photovoltaic output is considered and the prediction result has no further analyzation.…”

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confidence: 99%

The Photovoltaic Output Prediction Based on Variational Mode Decomposition and Maximum Relevance Minimum Redundancy

Zhang

Li³

et al. 2019

Applied Sciences

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Photovoltaic output is affected by solar irradiance, ambient temperature, instantaneous cloud cluster, etc., and the output sequence shows obvious intermittent and random features, which creates great difficulty for photovoltaic output prediction. Aiming at the problem of low predictability of photovoltaic power generation, a combined photovoltaic output prediction method based on variational mode decomposition (VMD), maximum relevance minimum redundancy (mRMR) and deep belief network (DBN) is proposed. The method uses VMD to decompose the photovoltaic output sequence into modal components of different characteristics, and determines the main characteristic factors of each modal component by mRMR, and the DBN model is used to fit the modal components and the corresponding characteristic factors, then the predicted results of each modal component is superimposed to obtain the predicted value of the photovoltaic output. By using the data of a certain photovoltaic power station in Yunnan for comparative experiments, it is found that the model proposed in this paper improves the prediction accuracy of photovoltaic output.

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