Photovoltaic power prediction based on principal component analysis and Support Vector Machine

Song, Qijun; Li, Fen; Qian, Jialin; Zhao, Jinbin; Chen, Zhenghong

doi:10.1109/isgt-asia.2016.7796490

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…Such models find it difficult to overcome issues in convergence and local optimization, and they suffer from large prediction errors, mainly because many network input compositions are present, along with noises in the sample data. Various scholars have used principal component analysis (PCA) [8] and other algorithms to preprocess the collected data; however, PCA and other algorithms find it difficult to process large amounts of data collected in real time due to their own defects. In view of the above problem, this paper adopted CCIPCA, which is suitable in analysing large data sets to extract key data affecting power generation prediction, to reduce dimensions in data and eliminate noise.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

Zhu

2020

Complexity

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In order to remedy problems encompassing large-scale data being collected by photovoltaic (PV) stations, multiple dimensions of power prediction mode input, noise, slow model convergence speed, and poor precision, a power prediction model that combines the Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) with Long Short-Term Memory (LSTM) network was proposed in this study. The corresponding model uses factor correlation coefficient to evaluate the factors that affect PV generation and obtains the most critical factor of PV generation. Then, it uses CCIPCA to reduce the dimension of PV super large-scale data to the factor dimension, avoiding the complex calculation of covariance matrix of algorithms such as Principal Component Analysis (PCA) and to some extent eliminating the influence of noise made by PV generation data acquisition equipment and transmission equipment such as sensors. The training speed and convergence speed of LSTM are improved by the dimension-reduced data. The PV generation data of a certain power station over a period is collected from SolarGIS as sample data. The model is compared with Markov chain power generation prediction model and GA-BP power generation prediction model. The experimental results indicate that the generation prediction error of the model is less than 3%.

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Section: Introductionmentioning

confidence: 99%

Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

Zhu

2020

Complexity

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“…(2) ough there are many influence factors affecting PV output, most of the forecasting methods lack the analyses of the correlations between the factors and the PV output. Reference [14] proposes a forecasting method based on Principal Component Analysis (PCA) and SVM. PCA is a statistical analysis method that transforms multiple influence factors into a few unrelated comprehensive variables but cannot select the most effective factors properly.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System

Deng

Fengshun

Zhang

et al. 2020

Journal of Electrical and Computer Engineering

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Accurate short-term power output forecasting results are conducive to reducing the scheduling difficulty of grid-connected operation of distributed photovoltaic (PV) systems, thus improving the safety and stability of power grid operation. In this paper, a one-day-ahead short-term power output forecasting model based on correlation analysis and combination algorithms for distributed PV system is proposed to solve the problems within the current methods. Firstly, the basic information of distributed PV system is introduced, and the main influence factors affecting the power output of distributed PV system are determined. Secondly, the influence factors with higher correlation with PV output are selected by Spearman rank-order correlation coefficient (SROCC) analysis in multiple timescales. Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. The case study analysis based on the actual data indicates that the ELM-LSTM forecasting model proposed in this paper has higher forecasting accuracy than the traditional forecasting methods.

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“…Similarly, the ANN method is also employed to implement the forecasting of the PV power outputs in [17,18]. Support vector machine(SVM) was also employed to learn and model the relationship and relevance between the input data such as solar radiation and the output of PV power in [19][20][21]. In [22,23], multiple linear regression (MLR) modeled the power outputs of PV system based on the features of solar radiation and the weather data.…”

Section: Introductionmentioning

confidence: 99%

An Bayesian Learning and Nonlinear Regression Model for Photovoltaic Power Output Forecasting

Gao

Chen

2020

Applied Mathematics and Nonlinear Sciences

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Photovoltaic power system is taking a significant percentage of power system and the demands for accurate forecasting of the power outputs is surging. In prior works, the forecasting problem was formulated as a regression problem, however, which most cannot guarantee that the forecasted outputs is nonnegative. To solve this problem, we proposed a novel probabilistic model by using nonlinear regression and Bayesian learning method. In the paper, we present the detailed theoretical derivations and interpretations. The simulation results show the validity and feasibility of the proposed algorithm by comparing with the traditional SVM algorithm.

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Photovoltaic power prediction based on principal component analysis and Support Vector Machine

Cited by 11 publications

References 9 publications

Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System

An Bayesian Learning and Nonlinear Regression Model for Photovoltaic Power Output Forecasting

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