A Chaotic Approach on Solar Irradiance Forecasting

TL;DR

This paper applies chaos theory, specifically the False Nearest Neighbour method, to analyze solar irradiance time series data, determining the optimal embedding dimension for improved forecasting.

Contribution

It introduces a chaos-based analysis approach to identify the embedding dimension for solar irradiance data, aiding in better forecasting models.

Findings

Optimal embedding dimension is 4 for the data analyzed.

Sampling intervals of 60 and 30 minutes yield consistent embedding dimensions.

Chaotic analysis can enhance solar irradiance forecasting accuracy.

Abstract

We analyse the time series of solar irradiance measurements using chaos theory. The False Nearest Neighbour method (FNN), one of the most common methods of chaotic analysis is used for the analysis. One year data from the weather station located at Nanyang Technological University (NTU) Singapore with a temporal resolution of $1$ minute is employed for the study. The data is sampled at $60$ minutes interval and $30$ minutes interval for the analysis using the FNN method. Our experiments revealed that the optimum dimension required for solar irradiance is $4$ for both samplings. This indicates that a minimum of $4$ dimensions is required for embedding the data for the best representation of input. This study on obtaining the embedding dimension of solar irradiance measurement will greatly assist in fixing the number of previous data required for solar irradiance forecasting.