Day-Ahead Solar Forecasting Based on Multi-level Solar Measurements

TL;DR

This paper introduces a multi-level measurement-based NARX model for day-ahead solar forecasting, demonstrating improved accuracy over simpler models across various weather conditions.

Contribution

It presents a novel multi-level measurement approach combined with a NARX model for enhanced solar power forecasting accuracy.

Findings

The model performs well under different weather conditions.

Multi-level measurements improve forecast accuracy.

Compared to single-level models, the proposed approach yields better results.

Abstract

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and achieve better forecasts. The proposed model consists of four stages of data preparation, establishment of fitting model, model training, and forecasting. The model is tested under different weather conditions. Numerical simulations exhibit the acceptable performance of the model when compared to forecasting results obtained from two-level and single-level studies.