Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

Photovoltaic power forecasting with a rough set combination method

Yang, Xiyun and Yue, Hong and Ren, Jie (2016) Photovoltaic power forecasting with a rough set combination method. In: Control 2016 - 11th International Conference on Control, 2016-08-31 - 2016-09-02.

[img]
Preview
Text (Yang-etal-Control-2016-Photovoltaic-power-forecasting-with-a-rough-set-combination)
Yang_etal_Control_2016_Photovoltaic_power_forecasting_with_a_rough_set_combination.pdf - Accepted Author Manuscript

Download (261kB) | Preview

Abstract

One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner.