The energy obtained from infinite natural sources such as wind power, hydropower, solar energy, geothermal energy, biomass, tidal power and wave power are called renewable energy. In the last two decades, renewable energy has become a serious energy source. Between the years 1990 and 2014, total renewable electricity generation enlarged by 191% [1]. Europe’s 28% of overall electricity generation is obtained from renewable energy in 2014 [1]. Although hydropower plants have the highest renewable energy generation capacity, wind and solar power generation capacities are significantly increasing in the recent years. Figure 1 shows the share of energy from renewable sources in gross final consumption of energy in 2011. Among the renewable energy sources, solar power has the highest increase capacity. In 2014, solar power generation capacity increased by 14.1% compared with 2013 [1]. Although there is a significant increase in the installed capacity of solar energy sources, the percentage of solar energy in the total installed capacity is still limited. Understanding and modeling the solar energy capacity can enhance the solar energy production. The factors and the relations among the solar energy generation are uncertain, complex and vague. Fuzzy cognitive maps are excellent tools for dealing with complexity and ambiguity inherent in modeling problems [2]. Hesitant fuzzy sets are the extensions of fuzzy sets where more than one membership value of an element can be defined [3,4]. Hesitant fuzzy sets and hesitant linguistic term sets enable better expressing the experts’ evaluations [4–۶]. In this study, hesitant fuzzy cognitive maps are used for modeling solar energy generation capacity. In general, this study examines the dynamic behavior of factors whose behavior is unclear during the installation phase of renewable energy systems. In this way, the relationships between the factors are determined linguistically by expert opinions and the behavior of the factors and the general system can be observed. This model can be differentiated according to the opinions of experts which vary according to economic, social, political and environmental conditions. The rest of the paper is organized as follows: In Sect. “Fuzzy cognitive map”, preliminaries for fuzzy cognitive maps are given. Section “Hesitant Fuzzy cognitive map (HFCM)” introduces the hesitant fuzzy cognitive maps (HFCMs). In Sect. “Modeling renewable energy usage based on HFCMs”, the proposed hesitant fuzzy renewable energy cognitive map with different scenarios is given. In the last section, conclusions and further suggestions are given.