Photovoltaic power prediction based on hybrid modeling of neural network and stochastic differential equation

Ying, Zhang; Kong, Laiqiang

doi:10.1016/j.isatra.2021.11.008

Cited by 22 publications

(14 citation statements)

References 31 publications

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“…The traditional photovoltaic power generation forecasting methods include time series [1,2], grey theory [3][4][5], regression analysis [6][7][8], and neural network methods [9][10][11].…”

Section: Traditional Photovoltaic Power Generation Forecasting Methodsmentioning

confidence: 99%

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Fan¹,

Hui-zhen²,

Chen³

et al. 2022

Glob. J. Energ. Technol. Res. Updat.

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With the continuous expansion of the capacity of photovoltaic power generation systems, accurate power generation load forecasting can make grid dispatching more reasonable and optimize load distribution. This paper proposes a combined forecasting model based on Auto Regression Integrate Moving Average (ARIMA), back propagation neural network (BPNN), and support vector regression (SVR), namely ARIMA-BPNN-SVR model, aiming at the problem of low accuracy of a single model and traditional forecasting model. Through the complementary advantages of ARIMA, BPNN, and SVR models, the model has good anti-noise ability, nonlinear mapping, and adaptive ability when processing photovoltaic power generation data. Data experiments are carried out on solar photovoltaic power generation in the United States, and the accuracy of model forecasting is evaluated according to MAE, MSE, RMSE, and MAPE. The experimental results show that the proposed ARIMA-BPNN-SVR outperforms the forecasting performance of the single models ARIMA, BPNN, and SVR. Its MAE, MSE, RMSE and MAPE are 0.53, 0.41, 0.64 and 0.84 respectively. In the Wilcoxon sign-rank test, the p-value of the proposed model reached 0.98, indicating the effectiveness of the ARIMA-BPNN-SVR model.

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“…The traditional photovoltaic power generation forecasting methods include time series [1,2], grey theory [3][4][5], regression analysis [6][7][8], and neural network methods [9][10][11].…”

Section: Traditional Photovoltaic Power Generation Forecasting Methodsmentioning

confidence: 99%

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Fan¹,

Hui-zhen²,

Chen³

et al. 2022

Glob. J. Energ. Technol. Res. Updat.

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“…Badosa et al (2018) applied similar modeling approaches as Møller et al (2016) used for wind power forecasting to a solar power forecasting application. SDEs have also been used in hybrid methods with other forecasting tools, such as Zhang and Kong (2021) who used SDEs alongside recurrent neural networks to forecast solar power production.…”

Section: Multivariate Probabilistic Forecasting Using Sdesmentioning

confidence: 99%

Recent developments in multivariate wind and solar power forecasting

Sørensen

Nystrup

Bjerregård

et al. 2022

WIREs Energy & Environment

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The intermittency of renewable energy sources, such as wind and solar, means that they require reliable and accurate forecasts to integrate properly into energy systems. This review introduces and examines a selection of state-ofthe-art methods that are applied for multivariate forecasting of wind and solar power production. Methods such as conditional parametric and combined forecasting already see wide use in practice, both commercially and scientifically. In the context of multivariate forecasting, it is vital to model the dependence between forecasts correctly. In recent years, reconciliation of forecasts to ensure coherency across spatial and temporal aggregation levels has shown great promise in increasing the accuracy of renewable energy forecasts. We introduce the methodology used for forecast reconciliation and review some recent applications for wind and solar power forecasting. Many forecasters are beginning to see the benefit of the greater information provided by probabilistic forecasts. We highlight stochastic differential equations as a method for probabilistic forecasting, which can also model the dependence structure. Lastly, we discuss forecast evaluation and how choosing a proper approach to evaluation is vital to avoid misrepresenting forecasts.

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“…The original PV power generation data are collected from July to September 2017 and July to November 2016 from a roof PV power plant in eastern China. The dataset [29] is accessible from the Power and Energy datasets of IEEE DataPort. Each dataset is 7.5 min intervals of PV power.…”

Section: Data Description and Analysismentioning

confidence: 99%

“…The LSTM-CNN model outperforms CNN-LSTM in terms of performance and computational time. In [29], an effective PV power forecasting framework was developed based on wavelet analysis and LSTM with a stochastic differential equation to provide accurate prediction information in different seasons. Pi et al [16] used multichannel wavelet transform combining CNN and BiLSTM networks to forecast solar irradiance under different time horizons.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods

Guo

Yan

2022

Sensors

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The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the Bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods.

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Photovoltaic power prediction based on hybrid modeling of neural network and stochastic differential equation

Cited by 22 publications

References 31 publications

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Recent developments in multivariate wind and solar power forecasting

Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods

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