Use of SARIMAX Model for Solar PV Power Output Forecasting in Baguio City, Philippines

Abstract

Over the past few decades, there has been a continuous increase in the public interest for solar energy as an alternative and cleaner source of energy. Therefore, it is not surprising that there is a similar interest in developing accurate models to forecast solar photovoltaic (PV) power production. Such models vary depending on the geographical location of PV sites and the seasons considered. The Philippines has yet to have a solar PV output forecasting model adapted to the country's local conditions. This study aims to evaluate the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) forecasting model as a tool for forecasting solar PV generation based on the seasonal characteristics of the country and identify which input parameters are significant for each season. This work used solar PV production data as an endogenous variable. Meanwhile, exogenous variables include in-situ solar irradiance data from solar power plants; and cloud cover, wind speed and direction, ambient temperature, precipitation, and relative humidity, which we extracted from ERA5 Reanalysis data. Datasets were divided based on the seasons as defined by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), namely, hot dry (HD), rainy (R), and cold dry (CD) seasons. Then, we performed a forecast on each season and one full year to assess the performance of the SARIMAX model. The best model per season was done based on the forecasting accuracy measured using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). This work analyzed seasonal and year-round SARIMAX models for Baguio City, Philippines. Results show that the cold dry season got the highest accuracy value in terms of 2.26 MAE and 4.06 RMSE. Meanwhile, the rainy season had the lowest accuracy of 12.91 MAE and 16.16 RMSE. We can infer from our findings that seasonal forecasting is better during hot dry and cold dry seasons. We also found that the year-round forecasting model performs better than the rainy season model. From the significant parameters identified in our best models, our analysis showed that wind direction can be removed from all models; irradiation and relative humidity were significant for all seasons.