A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

Mei, Fei; Pan, Yi; Zhu, Kunpeng; Zheng, Jianhua

doi:10.3390/su10030820

Cited by 21 publications

(11 citation statements)

References 31 publications

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“…e Online-SVR algorithm, an online regression modelling method, is a time series analysis method, which can realize online dynamic update [32,33]. e main difference between the Online-SVR model and traditional SVR model is the way of data processing [34].…”

Section: Online-svr Modelmentioning

confidence: 99%

A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

Mao

et al. 2021

Mathematical Problems in Engineering

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Fiber optic gyroscope (FOG) inertial measurement unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in positioning and navigation of military and aerospace fields, due to its simple structure, small size, and high accuracy. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. In order to reduce the FOG drift and improve the navigation accuracy, a long short-term memory recurrent neural network (LSTM-RNN) model is established, and a real-time acquisition method of the temperature change rate based on moving average is proposed. In addition, for comparative analysis, backpropagation (BP) neural network model, CART-Bagging, classification and regression tree (CART) model, and online support vector machine regression (Online-SVR) model are established to filter FOG outputs. Numerical simulation based on field test data in the range of -20°C to 50°C is employed to verify the effectiveness and superiority of the LSTM-RNN model. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. This proposed solution can be applied in military and aerospace fields.

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Section: Online-svr Modelmentioning

confidence: 99%

A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

Mao

et al. 2021

Mathematical Problems in Engineering

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“…Regarding the time scale, the forecastings can be divided into: ultra-short-term forecastings (a few minutes to 1 hour ahead), short-term forecastings (1 hour to several hours ahead), medium-term forecastings (several hours to 1 week ahead), and long-term forecastings (1week to 1 year or more ahead) [6]. In terms of the size of a spatial range, forecasting can be obtained for a single area or a regional area [7]. In the literature, several forecasting methods for prediction of a PV output power have been introduced.…”

Section: Introductionmentioning

confidence: 99%

“…The direct forecasting models represent the regression models based on the usage of instantaneous power information which is established from the associated data [8] such as solar radiance, module temperature, humidity, wind speed, and so on. These data are supplied by the PV power plants or from numerical weather prediction (NWP) data [7], [9]. Modeling methods include the artificial neural networks (ANNs) [10]- [12], support vector machine (SVM) [13], multivariate regression [14] methods, and other methods [9].…”

Section: Introductionmentioning

confidence: 99%

Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting

2019

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With the fast expansion of renewable energy system installed capacity in recent years, the availability, stability, and quality of smart grids have become increasingly important. The renewable energy output forecasting applications have also been developing rapidly in recent years, and such techniques have particularly been applied in the fields of wind and solar photovoltaic (PV). In the case of solar PV output forecasting, many applications have been performed with machine learning and hybrid techniques. In this paper, we propose a high-precision deep neural network model named PVPNet to forecast PV system output power. The methodology behind the proposed model is based on deep neural networks, and the model is able to generate a 24-h probabilistic and deterministic forecasting of PV power output based on meteorological information, such as temperature, solar radiation, and historical PV system output data. The forecasting accuracy of PVPNet is determined by the mean absolute error (MAE) and root mean square error (RMSE) values. The results from the experiments show that the MAE and RMSE of the proposed algorithm are 109.4845 and 163.1513, respectively. The results prove that the prediction accuracy of the PVPNet outperforms other benchmark models, and the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity. INDEX TERMS Deep neural network, photovoltaic output power forecasting, photovoltaic system, renewable energy sources.

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“…The extraterrestrial solar irradiance G 0 and the surface solar irradiance G s were used to define the metric to describe different weather regimes, since the distinctions between G 0 and G s can reflect the change of weather regimes [2]. The kernel fuzzy c-means (KFCM) was utilized in [18] to classify the characteristic data of different weather regimes. Statistical values of five weather variables, including the maximum irradiance, the maximum temperature, the maximum fluctuation, mean fluctuation, standard deviation of fluctuation, and maximum third derivative of fluctuation, were used as inputs to KFCM.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model

Pan

Tan

2019

IEEE Access

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Accurate solar generation prediction is of great significance for grid dispatching and operation of photovoltaic power plants. In this paper, we propose a novel solar generation forecasting method based on cluster analysis and ensemble model. Two common ways to improve prediction accuracy are adopted. We first conduct cluster analysis based on solar generation to obtain a weather regime, which improves the computational efficiency and avoids the difficulty in selecting weather variables to participate in the clustering process. Then random forests with different parameters is established for different weather regimes, which is used as component models in the followed ensemble model. Finally, we weighted the predictions from different weather regimes to get the final results. To avoid manual design weights, ridge regression is used to calculate weights for each weather regime automatically. A large number of experiments have been carried out on freely available data sets to verify the performance of the proposed method. The experimental results show that our method predicts solar generation more accurately, which has broad prospects in practical application. INDEX TERMS Cluster analysis, ensemble model, ridge regression, solar generation forecasting.

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A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

Cited by 21 publications

References 31 publications

A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting

Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model

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