Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

Zhang, Qiang; Liu, Gang; Wei, Xiangzhong

doi:10.1051/e3sconf/202125201056

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Photovoltaic power generation has a high degree of similarity when the climatic kinds are the same, and the power generation is strongly tied to different meteorological characteristics [6][7][8][9][10][11]. For various types of weather data, cluster analysis of historical data and selection of related day sample data can therefore effectively increase prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Short-term Power Prediction Method of Photovoltaic Based on Output Clustering in Smart Grid

Fan¹,

He²,

Guo³

et al. 2023

AETR

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In order to eliminate the influence of axle load transfer on the bench test of motorcycle braking performance, a method of simulating axial load transfer on the bench was developed and testing equipment was constructed.The comparison tests of road condition, bench without axle load transfer condition and bench with axle load transfer condition were carried out under three braking modes respectively. After simulating axle load transfer, the results of bench test are closer to the road test.In front braking mode,the average deceleration deviation rate, braking distance deviation rate and braking time deviation rate of bench test with simulating axial load decreased by 6.8%, 6.4% and 4.7%. In back braking mode,the average deceleration deviation rate, braking distance deviation rate and braking time deviation rate of bench test with simulating axial load decreased by 8.9%, 7.9% and 10.2%.In front-back braking mode,the average deceleration deviation rate, braking distance deviation rate and braking time deviation rate of bench test with simulating axial load decreased by 7.3%, 6.1% and 6.5%The accuracy of the bench test of motorcycle braking performance were effectively improved.

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Section: Introductionmentioning

confidence: 99%

Short-term Power Prediction Method of Photovoltaic Based on Output Clustering in Smart Grid

Fan¹,

He²,

Guo³

et al. 2023

AETR

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“…With the rapid development of photovoltaic power generation, the impact of largescale photovoltaic grid connections on the power system is becoming more and more obvious, and accurate short-term photovoltaic power generation prediction can effectively alleviate the pressure caused by photovoltaic grid connections on the power system, which is of great significance to ensure the stable operation of the power grid and the reasonable allocation of resources [1,2]. Therefore, obtaining reliable data for the power generation forecast of photovoltaic power plants has also become an important issue [3].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Generation Prediction Model Based on Improved Data Decomposition and Time Convolution Network

Cao,

Tian,

et al. 2023

Energies

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In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research.

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Short-Term Photovoltaic Power Prediction Based on NGO-LSTM Neural Network

于

2023

MOS

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Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

Cited by 3 publications

References 9 publications

Short-term Power Prediction Method of Photovoltaic Based on Output Clustering in Smart Grid

Short-term Power Prediction Method of Photovoltaic Based on Output Clustering in Smart Grid

Short-Term Photovoltaic Power Generation Prediction Model Based on Improved Data Decomposition and Time Convolution Network

Short-Term Photovoltaic Power Prediction Based on NGO-LSTM Neural Network

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