# Model Estimation for Solar Generation Forecasting using Cloud Cover Data

## TL;DR

This paper introduces a simple, efficient parametric model for forecasting solar power generation using cloud cover data, suitable for large-scale PV integration without needing local meteorological measurements.

## Contribution

It proposes a novel model that leverages cloud cover data and power measurements, enabling large-scale PV forecasting without local meteorological sensors.

## Key findings

- Model achieves accurate forecasts with low complexity.
- Validated with both simulated and real data.
- Suitable for large-scale PV plant integration.

## Abstract

This paper presents a parametric model approach to address the problem of photovoltaic generation forecasting in a scenario where measurements of meteorological variables, i.e., solar irradiance and temperature, are not available at the plant site. This scenario is relevant to electricity network operation, when a large number of PV plants are deployed in the grid. The proposed method makes use of raw cloud cover data provided by a meteorological service combined with power generation measurements, and is particularly suitable in PV plant integration on a large-scale basis, due to low model complexity and computational efficiency. An extensive validation is performed using both simulated and real data.