First and second order semi-Markov chains for wind speed modeling

TL;DR

This paper introduces semi-Markov chain models to generate synthetic wind speed data that better replicate real statistical properties compared to traditional Markov models, aiding renewable energy planning.

Contribution

The paper proposes three semi-Markov models for wind speed simulation, improving statistical accuracy over standard Markov chain approaches.

Findings

Semi-Markov models better reproduce wind speed autocorrelation.

Synthetic data closely matches real wind speed statistics.

Models outperform simple Markov chain in statistical fidelity.

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. 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. With the aim of reproducing the statistical properties of this data we propose the use of three semi-Markov models. We 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 models with those of real data and also with a synthetic time series generated though a simple Markov chain.