ACGL-TR: A deep learning model for spatio-temporal short-term irradiance forecast

• Published in: Energy conversion and management Vol. 284; p. 116970
• Main Authors: Shan, Shuo, Ding, Zhetong, Zhang, Kanjian, Wei, Haikun, Li, Chenxi, Zhao, Qibin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2023
• Subjects: Deep learning; Distributed photovoltaic systems; Spatio-temporal short-term solar irradiance forecast; Tensor regression; Deep learning; Tensor regression; Distributed photovoltaic systems; Spatio-temporal short-term solar irradiance forecast
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2023.116970

With the vigorous development of renewable energy, the installed capacity of photovoltaic (PV) power plants continuously expands. For distributed PV systems, spatio-temporal short-term solar irradiance forecasting can give forecasts for multiple sites simultaneously and help improve the generation of the PV power. However, it is difficult to explore appropriate spatio-temporal relationship for high-dimensional and long-historical data. Thus, an Attention Graph Convolution Long short term memory neural network with Tensor Regression (AGCL-TR) is proposed. Firstly, the adaptive adjacent matrices, which represents the relationship among PV sites, are obtained directly from the encoded feature of the history meteorological data of multiple PV sites by self-attention mechanism. Secondly, graph feature along with original data are handled by AGCL to obtain spatio-temporal feature. Thirdly, a TR network is used to decode the higher-order spatio-temporal feature into next step irradiance forecasts for each PV site. The proposed method is validated on an open access dataset, the National Solar Radiation Database (NSRDB), with eight sites randomly selected in Nevada, North America. The experiment demonstrates that the proposed method outperforms the existing spatio-temporal prediction methods in each site, with an improvement of RMSE of 11.9%–19.8%. The proposed method can effectively extract and preserve the spatio-temporal features of high-dimensional data, which can improve the prediction performance in distributed PV systems. [Display omitted] •A novel model for short-term spatio-temporal irradiance forecast is proposed•Self-attention-based adjacent matrix is introduced to represent spatial interaction.•Graph feature is extracted to enrich spatial feature representation.•Spatio-temporal feature is preserved from compressing by tensor regression network.