Hybrid deep neural model for hourly solar irradiance forecasting

Huang, Xiaoqiao; Li, Qiong; Tai, Yonghang; Chen, Zaiqing; Zhang, Jun; Shi, Junsheng; Gao, Bixuan; Liu, Wu-Ming

doi:10.1016/j.renene.2021.02.161

Cited by 93 publications

(17 citation statements)

References 61 publications

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“…LSTM is a common and established approach that uses time-sequence data to forecast future occurrences [ 35 , 36 ]. A typical LSTM unit consists of an input and output gate, forget gate, unit input, and cell state.…”

Section: Methodsmentioning

confidence: 99%

Impact analysis of COVID-19 pandemic on the future green power sector: A case study in the Netherlands

Luo

et al. 2022

Renewable Energy

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Section: Methodsmentioning

confidence: 99%

Impact analysis of COVID-19 pandemic on the future green power sector: A case study in the Netherlands

Luo

et al. 2022

Renewable Energy

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“…To this end, we have chosen four metrics in order to evaluate the forecasted data noted as 𝑦 ̂ versus measured one noted 𝑦 and a number of observations noted 𝑁. These statical metrics are summarized as follows (Benmouiza and Cheknane 2018;Botchkarev 2019;Huang et al 2021) ;…”

Section: Error Metricsmentioning

confidence: 99%

“…multivariate time series data: the BP-MLP model, the RNN-MLP model, the LSTM-MLP model , and WPD-CNN-LSTM-MLP(Huang et al 2021) . Moreover, clustered ANFIS network using fuzzy c-means, subtractive clustering, and…”

mentioning

confidence: 99%

Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

Benmouiza

2022

Environ Sci Pollut Res

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The foundation for many solar energies uses as well as economic and environmental concerns is global solar irradiation information. However, due to solar irradiation and measurements variations, reliable worldwide statistics on solar irradiation are frequently impossible or difficult to acquire. In addition, more precise forecast of solar irradiation plays an increasingly important role in electric energy planning and management due to integrating photovoltaic solar systems into power networks. Hence, this paper proposes a new hybrid model for 1-hour ahead solar irradiation forecasting called LGC-GMDH (local gravitational clustering-Group method of data handling). The novel LGC-GMDH model is based on the local clustering that adequately captures the underlying features of the solar irradiation time series. Each cluster is then forecasted using the GMDH method, which is a self-organized system that is capable of handling very complicated nonlinear problems. Finally, these local forecasts are reconstructed in order to obtain the global forecast. Comparative study between the proposed model and the traditional individual models such as; backpropagation neural network (BP), supporting vector machines (SVM), long short-term memory (LTSM), hybrid models such; BP-MLP, RNN-MLP, LSTM-MLP hybrid wavelet packet decomposition (WPD), convolutional neural network (CNN) with LSTM-MLP, and ANFIS clustering shows that the proposed model overcomes conventional model deficiencies and achieves more precise predicting outcome.

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“…This makes the conventional models far from understanding the weather conditions of observation stations and their spatiotemporal correlations. Cheng et al [ 49 ] used GNN to analyze correlations between atmospheric variables, and Huang et al [ 50 ] extracted the temporal features of each variable and analyzed feature correlations using MLP. However, these studies omitted the spatial influences between observation stations.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network

Jeon

Choi

Lee

2022

Sensors

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Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.

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Hybrid deep neural model for hourly solar irradiance forecasting

Cited by 93 publications

References 61 publications

Impact analysis of COVID-19 pandemic on the future green power sector: A case study in the Netherlands

Impact analysis of COVID-19 pandemic on the future green power sector: A case study in the Netherlands

Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network

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