Wind speed modeled as an indexed semi-Markov process

TL;DR

This paper introduces an indexed semi-Markov process model to accurately generate synthetic wind speed data, capturing statistical properties better than traditional models, validated through real data from Italy.

Contribution

The paper presents a novel indexed semi-Markov process model that improves the reproduction of wind speed data's statistical features compared to previous semi-Markov models.

Findings

The model accurately reproduces the autocorrelation of real wind speed data.

Synthetic data generated matches statistical properties of real data.

Indexed semi-Markov process outperforms simple semi-Markov models.

Abstract

The increasing interest in renewable energy, particularly in wind, has given rise to the necessity of accurate models for the generation of good synthetic wind speed data. Markov chains are often used with this purpose but better models are needed to reproduce the statistical properties of wind speed data. In a previous paper we showed that semi-Markov processes are more appropriate for this purpose but to reach an accurate reproduction of real data features high order model should be used. In this work we introduce an indexed semi-Markov process that is able to fit real data. We downloaded a database, freely available from the web, in which are included wind speed data taken from L.S.I. -Lastem station (Italy) and sampled every 10 minutes. We then generate synthetic time series for wind speed by means of Monte Carlo simulations. The time lagged autocorrelation is then used to compare statistical properties of the proposed model with those of real data and also with a synthetic time series generated though a simple semi-Markov process.