A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix

Fu, Xingbo; Gao, Feng; Wu, Jiang; Guan, Xiaohong; Li, Xuan; Liu, Pengyuan; Li, Pai

doi:10.1109/wcica.2018.8630379

Cited by 2 publications

(1 citation statement)

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“…We presented a simulation method of solar irradiance data of one station based on feature clustering and Markov chain in our early works [5]. This method may be used to generate long samples of solar radiation and it can deal with the situation where we simulate one-station solar irradiance data.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Approach to Multi-station Solar Irradiance Data Considering Temporal Correlations

Gao

et al. 2019

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

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Solar energy is one of important renewable energy sources and simulation of solar irradiance can be used as input for simulation of photovoltaic (PV) generation. This paper proposes a simulation algorithm of multi-station solar irradiance data considering temporal correlations. First of all, we group all the days of the observed data to k clusters for each station based on their daily features of solar irradiance and the daily states constitute Markov chain of days. Then, we reduce state permutations of different stations before getting Markov Transition Probability Matrix (MTPM). In terms of the observed data and MTPM, the simulation approach is proposed. Finally, we test our approach by applying to solar irradiance data of three stations and show that the properties of simulated data match those of the observed data.

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Section: Introductionmentioning

confidence: 99%