This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in predicting solar PV output using one-year data from three solar PV installations in the Philippines. The research aims to compare the performance of these models with their hybrid counterparts and investigate their performance. The study utilizes the adjusted shortwave radiation (SWR) product in the Advanced Himawari Imager 8 (AHI-8), as a proxy for in situ solar irradiance, and weather parameters, to improve the accuracy of the forecasting models. The results show that SARIMAX outperforms LSTM, XGBoost, and their combinations for Plants 1 and 2, while XGBoost performs best for Plant 3. Contrary to previous studies, the hybrid models did not provide more accurate forecasts than the individual methods. The performance of the models varied depending on the forecasted month and installation site. Using adjusted SWR and other weather parameters, as inputs in forecasting solar PV output, adds novelty to this research. Future research should consider comparing the accuracy of using adjusted SWR alone and combined with other weather parameters. This study contributes to solar PV output forecasting by utilizing adjusted satellite-derived solar radiation, and combining SARIMAX, LSTM, and XGBoost models, including their hybrid counterparts, in a single and comprehensive analysis.

This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. The accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.

In regions heavily affected by recurrent typhoons, the need for more resilient electricity infrastructure is pressing. This emphasizes the importance of integrating resilience assessment, including incorporating resilience metrics, into the planning process of power distribution systems against any disruptive events. Although standardized metrics exist for assessing distribution system reliability, the absence of formalized resilience metrics hampers informed investments in critical infrastructure such as microgrid development. In this work, a set of resilience metrics is proposed by reconceptualizing reliability metrics. The metrics were formulated to account for both the type of extreme event and its specific impact on loads with varying levels of criticality. The effectiveness of the proposed metrics is demonstrated through a Philippine microgrid case study. A Monte Carlo framework incorporating an extreme event model, component fragility model, and system response model was used to quantify the resilience improvement before and after stand-alone microgrid operation of the power distribution system. Results show that the proposed metrics can effectively evaluate resilience enhancement and highlight the value of a holistic approach of considering critical loads and types of extreme events to strengthen societal and community resilience, making a compelling case for strategic investments in infrastructure upgrades such as microgrids.

Forecasting models are often constrained by data availability, and in forecasting solar photovoltaic (PV) output, the literature suggests that solar irradiance contributes the most to solar PV output. The objective of this study is to identify which between the satellite-based and reanalysis solar irradiance data, namely, short wave radiation (SWR) and surface solar radiation downward (SSRD), respectively, is a better alternative to in situ solar irradiance in forecasting solar PV output should the latter become unavailable. Nine seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models were presented in this study to assess the forecasting performance of each solar irradiance data together with weather parameters. Using only historical data to forecast solar PV output, three seasonal autoregressive integrated moving average (SARIMA) models were run to forecast solar PV output and to compare and validate the efficacy of the SARIMAX models. The analysis was divided into seasons as defined by the Philippine Atmospheric, Geophysical and Astronomical Services Administration: hot dry, rainy, and cool dry. Results show that the use of SSRD is a better alternative than SWR when forecasting solar PV output for the hot dry season and cool dry season. For the hot dry season, SSRD has an root mean square error (RMSE) value of 0.411 kW while SWR has 0.416 kW. For the cool dry season, SSRD has an RMSE value of 0.457 kW while SWR has 0.471 kW. Meanwhile, SWR outperforms SSRD when forecasting solar PV output during the rainy season, with RMSE values at 0.375 and 0.401 kW, respectively.

Increased utilization of renewable energy (RE) resources is critical in achieving key climate goals by 2050. The intermittent nature of RE, especially solar and wind, however, poses reliability concerns to the utility grid. One way to address this problem is to harmonize the RE resources using spatio-temporal complementarity analysis. Two RE resources are said to be complementary if the lack of one is balanced by the abundance of the other, and vice versa. In this work, solar–wind complementarity was analyzed across the provinces of Kalinga and Apayao, Philippines, which are potential locations for harvesting RE as suggested by the Philippine Department of Energy. Global horizontal irradiance (GHI) and wind speed data sets were obtained from the NASA POWER database and then studied using canonical correlation analysis (CCA), a multivariate statistical technique that finds maximum correlations between time series data. We modified the standard CCA to identify pairs of locations within the region of study with the highest solar–wind complementarity. Results show that the two RE resources exhibit balancing in the resulting locations. By identifying these locations, solar and wind resources in the Philippine islands can be integrated optimally and sustainably, leading to a more stable power and increased utility grid reliability.

The sustainable energy transition in the Philippines requires accurate forecasting of solar PV output to optimize energy efficiency and grid management. While existing studies have emphasized the positive correlation between solar irradiance and PV production, this study aims to explore whether forecasting improves with the inclusion of weather data. This research conducts a comparative analysis between relying solely on solar irradiance against integrating various weather parameters to enhance solar PV output forecasting. The study focuses on three distinct locations (Pangasinan, Negros Occidental, and Davao Del Norte) and employs two models per each site: Model 1 (M1), which relies only on solar irradiance as predictors, and Model 2 (M2), which incorporates solar irradiance and weather parameters. Using Fifth Generation ECMWF Reanalysis (ERA5) Data, Principal Component Analysis (PCA) is conducted on the significant weather parameters. Extreme Gradient Boosting (XGBoost) with 5-fold nested cross-validation is applied for solar PV output forecasting. Models are assessed using Mean Absolute Percentage Error (MAPE) and skill scores. Results showthat while solar irradiance alone suffices for predicting solar PV output in Negros Occidental, incorporating weather parameters improves forecasting accuracy in Davao Del Norte and Pangasinan. This paper recommends caution in generalizing the findings to different regions with varying weather patterns, as the forecasting performance of the models is influenced by data quality, specific location, and prevailing weather conditions.