Chen, Lei, Li, Yangluxi, Du, Hu ORCID: https://orcid.org/0000-0002-1637-0626 and Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 2021. Solar radiation nowcasting through advanced CNN model integrated with ResNet structure. Presented at: Building Simulation 2021, Bruges, Belgium, 1-3 September 2021. Proceedings 17th International Conference of the International Building Performance Simulation Association (Building Simulation 2021). International Building Performance Simulation Association, |
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Abstract
Although a range of solar radiation forecasting methods have been developed for predicting photovoltaic generation, only a few of them focus on solar radiation forecasting for building energy demand. From the perspective of building performance modelling, solar radiation forecasting needs to meet several critical requirements including high spatial resolution (1m-2km) and high temporal resolution (5-60mins), the accurate value of Direct Normal Irradiance (DNI) and Diffuse Horizontal Irradiance (DHI), which differs the requirement for predicting photovoltaic generation. As the geometric sum of DNI and DHI, accurate prediction of Global Horizontal Irradiance (GHI) with high spatial-temporal resolution tends to be the prerequisite for precise prediction of DNI and DHI. This research aims to construct a hybrid nowcasting model to predict GHI in high spatial-temporal resolution. In this article, the authors adopt an advanced Convolutional Neural Network (CNN) model with Residual Neural Network (ResNet) structure to identify the cloud image information and predict the GHI at 10 minutes intervals merely using cloud images captured by a ground-based sky camera. On this basis, several ResNet structures are compared to achieve the optimal nowcasting model for GHI. The results present that the ResNet structure can efficiently capture the cloud information and the ResNet-152 achieves better performance than other alternative structures on the nowcasting of GHI. Finally, the authors discussed the calculation of synchronous DNI and DHI using the predictive GHI and Dirint model, and the application of DNI and DHI as the input for the simulation of building energy management.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Completion |
Status: | In Press |
Schools: | Architecture |
Subjects: | T Technology > TH Building construction |
Publisher: | International Building Performance Simulation Association |
Funders: | European Union |
Date of First Compliant Deposit: | 5 October 2021 |
Last Modified: | 06 May 2023 01:19 |
URI: | https://orca.cardiff.ac.uk/id/eprint/143566 |
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