Christan Joshua C Natava
ABSTRACT
Forecasting models are used to produce Energy Planning Models (EPMs) in developing renewable energy farms. One such application is the forecasting of daily global solar radiation, which is the energy being converted into PV power. In this study, results show that Gaussian process regression (GPR) and nonlinear autoregressive network with exogenous inputs (NARX) are equally proficient forecasting models for daily global solar radiation in Dumaguete, Philippines using only a relatively small dataset. A long short-term memory (LSTM) network was also trained to forecast global solar radiation. Combining the two produced a NARX-LSTM hybrid with a root mean square error (RMSE) of approximately 0.10. The global solar radiation forecast from this study could be used in calculating the amount of energy the PV cells from solar energy farms can harness in a day. Furthermore, the forecasting model can be used in the further development and improvement of energy planning models.
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Index Terms
- Forecasting of daily global solar radiation in Dumaguete, Philippines using NARX-LSTM Hybrid Network
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