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This paper presents a collaborative effort to develop localized solar photovoltaic (PV) power output (PPV) forecasting models for the Philippines using geospatial data. It underlines the importance of solar energy in the country and discusses the opportunities and challenges associated with PPV forecasting. Project SINAG, a two-year research project, aimed to develop solar PV output forecasting models through a collaborative approach with academic institutions, solar energy industries, and government agencies. Actual PPV data from 43 solar PV installations were analyzed alongside meteorological data from the PAGASA weather bureau, ERA5, AHI-8, and FY- 4A. These datasets were filtered based on a one-year period to ensure quality. The study employed SARIMAX, LSTM, and XGBoost models individually and in hybrid models to develop the forecasting models. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a case study in Baguio City, the SARIMAX model exhibited strong seasonal dependence, providing more accurate forecasts in dry seasons than in wet seasons. Additionally, the forecasting accuracy of each model (SARIMAX, LSTM, and XGBoost) varied based on the month and location of the installation, emphasizing the need for local and season-based PPV forecasting models. Despite implementation challenges, such as collaboration arrangements, bureaucratic barriers, and budget constraints, the project produced thirteen research publications and provided data for three student theses. This paper also demonstrated diverse engagements and contributions that emphasize the significance of collaborative research in conducting nationwide-scale data-driven projects.

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.

Solar photovoltaic (PV) technology has been gaining popularity in the Philippines as an alternative source of sustainable energy. In such technology, module tilt angle and weather conditions are among the system parameters that have substantial impacts on PV system performance. Previous studies have considered either tilt angles or weather conditions, but not the combined impact of these two parameters on solar PV power output. The objective of this study is to examine the effects of weather variables and tilt angle on the output of solar photovoltaic (PV) systems in Muntinlupa City, the Philippines. Three 120W monocrystalline solar PV panels were used and set up to three different tilt angles (i.e., 5°, 10°, and 15°). The fifth generation of the ECMWF's global climate and weather reanalysis (ERA5) dataset was used to gather hourly weather information such as surface solar radiation, wind speed, wind direction, relative humidity, ambient temperature, and total precipitation. Three principal components (PC), which together account for 95% of the variability, were identified using principal components analysis (PCA), which was used to address multicollinearity among the weather parameters. To assess the effects of tilt angle, time, and PCs on solar PV production, analysis of covariance (ANCOVA) was carried out. Results show that all weather variables, except for wind speed and total precipitation, have a significant impact on solar PV production with configuration producing the best results. Moreover, a significant difference in mean solar PV production was observed among the three tilt angles. From 6:00 AM to 2:00 PM, solar PV output gradually increases and declines thereafter. Outputs this study can help in optimizing the design and configuration of solar PV systems in the Philippines by considering weather variables and module tilt angle. Lastly, this study provides useful information for system designers, installers, and policymakers in improving energy generation and utilization, encouraging the use of renewable energy sources, and advancing sustainable energy objectives.

The SINAG Web Portal, a web-based GIS platform that hosts spatial data and a forecasting model for solar PV installation assessment, serves as a repository for the products and outputs of the SolarPot and OutSolar components of a research project called Solar PV Resource and Installation Assessment Using Geospatial Technologies (SINAG). The website was developed with frontend and backend services which involved the use of a content management system, database systems, cloud virtual machines, Linux operating systems, programming languages, specifically Python and JavaScript, and a style scripting framework. All outputs generated by the two components of Project SINAG are stored on the dedicated cloud server of the website. It includes processed spatial data, a graphical summary of the created models, the ability to explore historical data, data requests and downloads, and the use of mapping tools. It is anticipated that the web portal will play a significant role in the assessment and decision-making process for selecting candidate sites for installing solar PV systems across the Philippine archipelago.

The balance between food security and energy security is a national issue of extreme importance. A more stable supply of electricity could be achieved as solar farms expand but at the expense of losing some of the prime agricultural lands which endangers availability of sufficient agricultural produce. This study aims to use ANN-Cellular Automata (CA) via the Geographic Information System (GIS) platform and remote sensing (RS) data to assess the impact of cropland transition to solar farms and other land use/land cover (LULC). Several remotely sensed data were processed including MODIS land cover data (MCD12Q1), VIIRS nighttime lights (VNL v2.1), Advanced Himawari Imager Shortwave Radiation (AHI-SWR) product, and population density (LandScan) as inputs to the Cellular Automata-Artificial Neural Network (CA-ANN) model to simulate LULC changes in Tarlac Province, Philippines via the Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS. For years 2019, 2023 and 2027 with 2015 as the base year, results showed an increasing trend for savannas and grassland with ΔLULC values of +11.4% to +15.1% and +0.2% to 3.5%, respectively. Meanwhile, a decreasing trend is observed for built-up/water, forest, and cropland with ΔLULC values of −3.0% to −6.3%, −8.5% to −21.1%, and −3.9% to −4.2%, respectively. Results also showed a conversion of a 100-ha area of croplands to solar farm from year 2019 to 2023 which translates to an estimated monetary loss from agricultural produce due to solar farm conversion amounting to Php 7,584,720.00 (~USD 138,000) which is equivalent to the total average annual income of about 67 families in Tarlac. Lastly, the simulated 2027 LULC map showed pixels with unrealistic conversions from solar farm (year 2023) to cropland (year 2027). To improve the model, it is recommended to add more spatial data to effectively capture factors that may contribute to the expansion of solar farms in the future. Moreover, high resolution LULC maps (vector maps if available) can be used instead of a course resolution satellite-derived raster data. Nonetheless, this study has demonstrated the use of RS, GIS and machine learning techniques to model cropland conversion to solar farms and other LULC classes. Results from this study can provide scientific data to policy makers, solar industry players and other relevant stakeholders in doing technoeconomic assessment of solar farm development and expansion considering its effect on energy security and food security towards national sustainable development.