Demonstrating measure-correlate-predict algorithms for estimation of wind resources in central Finland

In this study, measure-correlate-predict (MCP) algorithms - Simple Linear Regression and Variance Ratio Methods - for predicting wind speed were studied. The MCP algorithms were successfully used to predict missing wind speeds at two sites in Jyväskylä and Viitasaari, respectively. These two algorit...

Täydet tiedot

Bibliografiset tiedot
Päätekijä: Saengyuenyongpipat, Paitoon
Muut tekijät: Matemaattis-luonnontieteellinen tiedekunta, Faculty of Sciences, Fysiikan laitos, Department of Physics, University of Jyväskylä, Jyväskylän yliopisto
Aineistotyyppi: Pro gradu
Kieli:eng
Julkaistu: 2010
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/23245
Kuvaus
Yhteenveto:In this study, measure-correlate-predict (MCP) algorithms - Simple Linear Regression and Variance Ratio Methods - for predicting wind speed were studied. The MCP algorithms were successfully used to predict missing wind speeds at two sites in Jyväskylä and Viitasaari, respectively. These two algorithms used data from one of the site to predict missing wind speed data at the other site. The results obtained using the MCP methods were compared using metrics that showed the characteristics of the predicted data to be unbiased compared to measured data. From the data of this study, we also evaluated wind power density at both sites which categorized the local wind resources as poor since the determined wind power densities were less than 100 W/m2.