Solar Irradiance Forecasting Using Triple Exponential Smoothing

TL;DR

This paper introduces a triple exponential smoothing method for short-term solar irradiance forecasting, improving accuracy in predicting rapid fluctuations to aid PV system integration into the power grid.

Contribution

The paper presents a novel application of triple exponential smoothing for intra-hour solar irradiance forecasting, demonstrating superior performance over traditional methods.

Findings

TES outperforms persistence and average forecasting methods.

The approach accurately captures rapid irradiance fluctuations.

Forecasting up to 20 minutes ahead is feasible with high accuracy.

Abstract

Owing to the growing concern of global warming and over-dependence on fossil fuels, there has been a huge interest in last years in the deployment of Photovoltaic (PV) systems for generating electricity. The output power of a PV array greatly depends, among other parameters, on solar irradiation. However, solar irradiation has an intermittent nature and suffers from rapid fluctuations. This creates challenges when integrating PV systems in the electricity grid and calls for accurate forecasting methods of solar irradiance. In this paper, we propose a triple exponential-smoothing based forecasting methodology for intra-hour forecasting of the solar irradiance at future lead times. We use time-series data of measured solar irradiance, together with clear-sky solar irradiance, to forecast solar irradiance up-to a period of 20 minutes. The numerical evaluation is performed using 1 year of weather data, collected by a PV outdoor test facility on the roof of an office building in Utrecht University, Utrecht, The Netherlands. We benchmark our proposed approach with two other common forecasting approaches: persistence forecasting and average forecasting. Results show that the TES method has a better forecasting performance as it can capture the rapid fluctuations of solar irradiance with a fair degree of accuracy.