Renewable energy generation forecasting in Turkey via intuitionistic fuzzy time series approach

Recently, renewable energy generation (REG) forecasting has become a challenging task for effective energy management. To date, a number of time-series models utilizing physical models, statistical techniques, and artificial intelligence algorithms have been proposed to deal with this problem. Fuzzy time series (FTS) models stand out among these methods since they do not need extensive historical data or some statistical assumptions like the normal distribution. Because intuitionistic fuzzy sets incorporate non-membership values in addition to membership values, they provide more information than conventional fuzzy sets do. In this regard, this paper recommends a hybrid methodology that combines the intuitionistic FTS method with the intuitionistic fuzzy cmeans method for REG forecasting of Turkey between the years 2000-2020. According to the empirical results, it can be concluded that the presented integrated model showed good performance in terms of accuracy. The suggested framework is a useful tool not only for REG forecasting but also for other time series forecasting problems.

Automated summary

Recently, renewable energy generation (REG) forecasting has become a challenging task for effective energy management. Fuzzy time series (FTS) models stand out among these methods since they do not need extensive historical data or some statistical assumptions. In this paper, a hybrid methodology that combines the intuitionistic FTS method with the intuitionistic fuzzy cmeans method for REG forecasting of Turkey between the years 2000-2020 is recommended. The presented integrated model showed good performance and is useful for REG forecasting and other time series forecasting problems.