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Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors
| Content Provider | MDPI |
|---|---|
| Author | Ahn, Hyung Keun Park, Neungsoo |
| Copyright Year | 2021 |
| Description | Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R2-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy. |
| Starting Page | 436 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en14020436 |
| Journal | Energies |
| Issue Number | 2 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-01-15 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Internet of Things (iot) Photovoltaic Power Forecasting Algorithm Recurrent Neural Networks (rnn) |
| Content Type | Text |
| Resource Type | Article |
