Predicting Solar Irradiance in Singapore

TL;DR

This paper presents a time-series forecasting method for short-term solar irradiance prediction in Singapore, demonstrating improved accuracy over common baseline models using weather sensor data.

Contribution

A novel time-series approach for short-term solar irradiance forecasting in tropical regions, validated with real sensor data and benchmarked against standard models.

Findings

Achieved a root mean square error of 147 W/m^2 for 15-minute ahead predictions.

Demonstrated superior performance over persistence and average models.

Validated method in Singapore's tropical climate.

Abstract

Solar irradiance is the primary input for all solar energy generation systems. The amount of available solar radiation over time under the local weather conditions helps to decide the optimal location, technology and size of a solar energy project. We study the behaviour of incident solar irradiance on the earth's surface using weather sensors. In this paper, we propose a time-series based technique to forecast the solar irradiance values for shorter lead times of upto 15 minutes. Our experiments are conducted in the tropical region viz. Singapore, which receives a large amount of solar irradiance throughout the year. We benchmark our method with two common forecasting techniques, namely persistence model and average model, and we obtain good prediction performance. We report a root mean square of 147 W/m^2 for a lead time of 15 minutes.