A Simulation Approach to Multi-station Solar Irradiance Data Considering Temporal Correlations

TL;DR

This paper introduces a novel simulation algorithm for multi-station solar irradiance data that accounts for temporal correlations by clustering days and modeling their transitions with Markov chains, validated on real data.

Contribution

The paper presents a new simulation method that incorporates temporal correlations across multiple stations using clustering and Markov chain modeling, improving data realism.

Findings

Simulated data properties closely match observed data.

The approach effectively captures temporal correlations in solar irradiance.

Validated on three stations with promising results.

Abstract

Solar energy is one of important renewable energy sources and simulation of solar irradiance can be used as input for simulation of photovoltaic (PV) generation. This paper proposes a simulation algorithm of multi-station solar irradiance data considering temporal correlations. First of all, we group all the days of the observed data to k clusters for each station based on their daily features of solar irradiance and the daily states constitute Markov chain of days. Then, we reduce state permutations of different stations before getting Markov Transition Probability Matrix (MTPM). In terms of the observed data and MTPM, the simulation approach is proposed. Finally, we test our approach by applying to solar irradiance data of three stations and show that the properties of simulated data match those of the observed data.