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Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices.
- Source :
-
Information (2078-2489) . Nov2023, Vol. 14 Issue 11, p617. 18p. - Publication Year :
- 2023
-
Abstract
- Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder–decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20782489
- Volume :
- 14
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Information (2078-2489)
- Publication Type :
- Academic Journal
- Accession number :
- 173826569
- Full Text :
- https://doi.org/10.3390/info14110617