Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks

Gao, Bixuan; Huang, Xiaoqiao; Shi, Junsheng; Tai, Yonghang; Zhang, Jun

doi:10.1016/j.renene.2020.09.141

Cited by 267 publications

(82 citation statements)

References 50 publications

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“…To capitalize on the CNN and LSTM hybrid model's advantages, many implementations of CNN and LSTM hybrid deep learning models have been developed in a variety of domains. Bixuan et al [6] compared the forecasting performance of five different CNN-LSTM structures and proposed that the solar irradiance features be decomposed using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), and the prediction model of historical solar irradiance features obtained by multiple CNNs be fused into LSTM for optimal performance. Behnam et al [28] introduced a parallel deep LSTM-CNN (PLCNet) model that relied on the parallel CNN layer and LSTM as the upper layer to extract the features of load data, and then connected the LSTM layer and the Dense layer to anticipate the final load data.…”

Section: Related Workmentioning

confidence: 99%

“…The LSTM model accurately captures the time series' pattern information, whereas the CNN model extracts valuable features from the time series without requiring domain expertise. LSTMs are excellent at extracting temporal features, while CNNs are excellent at extracting spatial features [6]. Thus, by integrating the technological advantages of the two models, forecasting accuracy can be increased.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Convolution Feature Extraction and Recurrent Neural Network Dependent Model for Short-Term Load Forecasting

Goh

Liu

et al. 2021

IEEE Access

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Load forecasting is critical for power system operation and market planning. With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a difficult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model's superior performance, the proposed method is applied to Ireland's load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model's generalizability.INDEX TERMS short-term load forecast, deep learning, multi-head CNN-LSTM, multi-step load prediction.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Convolution Feature Extraction and Recurrent Neural Network Dependent Model for Short-Term Load Forecasting

Goh

Liu

et al. 2021

IEEE Access

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“…Mishra et al (2020) proposed a short-term solar radiation prediction model using WT-LSTM and achieved good results, showing that deep learning technology has great potential in solar radiation. A CEEMDAN-CNN-LSTM model is proposed by Gao et al (2020) for hourly multi-region solar irradiance forecasting, and the results present that the model can achieve more accurate prediction performance than other models.…”

Section: Introductionmentioning

confidence: 99%

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

et al. 2021

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Solar radiation is the Earth’s primary source of energy and has an important role in the surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and climate extremes. The accurate prediction of solar radiation is therefore very important in both the solar industry and climate research. We constructed 12 machine learning models to predict and compare daily and monthly values of solar radiation and a stacking model using the best of these algorithms were developed to predict solar radiation. The results show that meteorological factors (such as sunshine duration, land surface temperature, and visibility) are crucial in the machine learning models. Trend analysis between extreme land surface temperatures and the amount of solar radiation showed the importance of solar radiation in compound extreme climate events. The gradient boosting regression tree (GBRT), extreme gradient lifting (XGBoost), Gaussian process regression (GPR), and random forest models performed better (poor) prediction capabilities of daily and monthly solar radiation. The stacking model, which included the GBRT, XGBoost, GPR, and random forest models, performed better than the single models in the prediction of daily solar radiation but showed no advantage over the XGBoost model in the prediction of the monthly solar radiation. We conclude that the stacking model and the XGBoost model are the best models to predict solar radiation.

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“…The CNN-LSTM network is constructed to analyze the vibration signals; when the CNN method finishes the feature of one-dimensional singles, LSTM continues to process this important information for diagnosis classification [ 18 , 19 , 20 ]. LSTM can extract special correlations for the stronger self-learning ability of CNN for prediction, and these architectures produce many ideas that are useful for this paper [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Gao

Kim

2021

Sensors

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Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

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Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks

Cited by 267 publications

References 50 publications

Multi-Convolution Feature Extraction and Recurrent Neural Network Dependent Model for Short-Term Load Forecasting

Multi-Convolution Feature Extraction and Recurrent Neural Network Dependent Model for Short-Term Load Forecasting

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

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