Back to Search Start Over

Collaborative Research for the Development of Localized Solar PV Output Forecasting Models for the Philippines Using Geospatial Data

Authors :
J. A. Principe
J. A. Ibañez
I. B. Benitez
Source :
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-5-2024, Pp 143-150 (2024)
Publication Year :
2024
Publisher :
Copernicus Publications, 2024.

Abstract

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.

Details

Language :
English
ISSN :
21949042 and 21949050
Volume :
X-5-2024
Database :
Directory of Open Access Journals
Journal :
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.98713595210944aca411308c02142a10
Document Type :
article
Full Text :
https://doi.org/10.5194/isprs-annals-X-5-2024-143-2024