Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

Wang, Fei; Yu, Yili; Zhang, Zhanyao; Li, Jie; Zhen, Zhao; Li, Kangping

doi:10.3390/app8081286

Cited by 141 publications

(85 citation statements)

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“…These models are similar to ones mentioned in previous studies [26,41], but the LSTM-based model proposed in this study has a significant difference because it attempts to estimate hourly PV power output regardless for meteorological conditions and considering meteorological and seasonal trends. In addition, while some models attempt to predict solar radiation [42][43][44] rather than PV power output prediction, our study focuses on the direct estimation of PV power output.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

2019

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Recently, the prediction of photovoltaic (PV) power has become of paramount importance to improve the expected revenue of PV operators and the effective operations of PV facility systems.Additionally, the precise PV power output prediction in an hourly manner enables more sophisticated strategies for PV operators and markets as the electricity price in a renewable energy market is continuously changing. However, the hourly prediction of PV power outputs is considered as a challenging problem due to the dynamic natures of meteorological information not only in a day but also across days. Therefore, in this paper, we suggest three PV power output prediction methods such as artificial neural network (ANN)-, deep neural network (DNN)-, and long and short term memory (LSTM)-based models that are capable to understand the hidden relationships between meteorological information and actual PV power outputs. In particular, the proposed LSTM based model is designed to capture both hourly patterns in a day and seasonal patterns across days. We conducted the experiments by using a real-world dataset. The experimental results show that the proposed ANN based model fails to yield satisfactory results, and the proposed LSTM based model successfully better performs more than 50% compared to the conventional statistical models in terms of mean absolute error.Energies 2019, 12, 215 2 of 22 By extension, for the more sophisticated decisions of PV operators and the effective operations of electricity markets, the prediction of PV power outputs in a hourly level is crucially necessary [9]. Since the prices of electricity sold in the most electricity markets for renewable energy dynamically change over time according to the expected amount of generated electricity, the ability to predict PV power outputs for each particular hour in a day enables the adaptive adjustment of the transaction volumes with tailored schedules. Figure 1 shows typical examples of PV power outputs during two consecutive days in summer and winter in South Korea obtained from the PV power system considered in this research. Here, easy and hard seasons divide according to fluctuation levels of the PV power output. For example, as shown Figure 1, more fluctuation of the PV power output occurs in winter compared to summer, meaning it is harder to predict the PV power output in winter. Naturally, PV power output starts to be generated when the sun rises

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Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

2019

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“…As the main influence factor of PV power generation, the solar irradiance and its accurate forecasting are prerequisites for solar PV power forecasting. Authors in [24] proposed an improved LSTM model to enhance the accuracy of day-ahead solar irradiance forecasting, and the simulation results indicated that the proposed model has high superiority in the solar irradiance forecasting, especially under extreme weather conditions. A new method for 1-h-ahead PV power forecasting using deep LSTM networks was proposed [25], which can capture abstract concepts in the PV power sequences.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Gao

Hong

et al. 2019

Applied Sciences

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Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46-13.46%.

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“…To test the prediction effect of the model proposed in this paper, we compared the results of the following prediction models: (1) the single prediction models (ARMA, DBN) used in this paper; (2) the common neural network prediction model, RNN and Gradient Boost Decision Tree (GBDT) in literature [46] and [47], used on a representative basis; (3) the combined prediction model, Discrete Wavelet Transformation (DWT) in literature [48] and traditional EMD and EEMD are used on a representative basis. The prediction results for each model are shown in Figure 11.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

et al. 2018

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Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

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Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

Cited by 141 publications

References 50 publications

Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

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