Panagiotis Golemis
Research Subjects
Panagiotis Golemis has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Alexandros Tsagkaropoulos
Electronics Lab, Physics Department, National & Kapodistrian University of Athens, 157 84 Athens, Greece
1 shared publication - Christoforos Vasilakis
University of Athens
1 shared publication - Dionysios Reisis
Computer Center, Physics Department, National & Kapodistrian University of Athens, 15772 Athens, Greece
1 shared publication - Georgios Konstantoulakis
Inaccess Networks, 12 Sorou Str., 15125 Maroussi, Greece
1 shared publication - Georgios Venitourakis
Computer Center, Physics Department, National & Kapodistrian University of Athens, 15772 Athens, Greece
1 shared publication - Tzouma Amrou
Inaccess Networks, 12 Sorou Str., 15125 Maroussi, Greece
1 shared publication
Latest Publications
- Open Access
- 18 November 2023
- Published by MDPI AG in Information
- Vol. 14 (11) , 617
Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices
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.
- 18 November 2023
- Published by MDPI AG in Information
- Vol. 14 (11) , 617
Open AccessNeural Network-Based Solar Irradiance Forecast for Edge Computing Devices
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.