# Estimation of Photovoltaic Generation Forecasting Models using Limited Information

## TL;DR

This paper introduces a low-complexity algorithm for estimating photovoltaic generation models using limited data, specifically only power measurements, a clear-sky irradiance model, and temperature forecasts, validated on real data.

## Contribution

A novel estimation method for PV models that operates without on-site meteorological measurements, relying solely on power data and forecasted variables.

## Key findings

- Effective estimation with limited data
- Validated on real photovoltaic plant data
- Low computational complexity

## Abstract

This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e. solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. An extensive experimental validation of the proposed method on real data is also presented.