# An empiric-stochastic approach, based on normalization parameters, to simulate solar irradiance

## TL;DR

This paper introduces an empiric-stochastic method using normalization parameters and kernel density estimation to accurately simulate solar irradiance, aiding in photovoltaic system design and testing.

## Contribution

It presents a novel empiric-stochastic approach based on normalization parameters and probability density maps for solar irradiance estimation.

## Key findings

- Good agreement between simulated and experimental solar power data
- Method effectively estimates solar irradiance for system design
- Supports pre-installation testing of photovoltaic systems

## Abstract

The data acquisition of solar radiation in a locality is essential for the development of efficient designs of systems, whose operation is based on solar energy. This paper presents a methodology to estimate solar irradiance using an empiric-stochastic approach, which consists of the computation of normalization parameters from solar irradiance data. For this study, solar irradiance data was collected with a weather station during a year. Post-treatment included a trimmed moving average, to smooth the data, the performance a fitting procedure using a simple model, to recover normalization parameters, and the estimation of a probability density map by means of a kernel density estimation method. The normalization parameters and the probability density map allowed us to build an empiric-stochastic methodology that generates an estimate of the solar irradiance. In order to validate our method, simulated solar irradiance has been used to compute the theoretical generation of solar power, which in turn has been compared to experimental data, retrieved from a commercial photovoltaic system. Since the simulation results show a good agreement has been with the experimental data, this simple methodology can estimate the solar power production and may help consumers to design and test a photovoltaic system before installation.