Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems

Forecasting in Renewable Energy Systems is a challenging problem since their inputs present some uncertainties in the data distribution. On the other hand, there is an increasing volume of information recorded by such systems that can be explored by a forecasting model with the expectation of improved performance. This work introduces e-MVFTS (evolving Multivariate Fuzzy Time Series), an evolving forecasting model based on Fuzzy Time Series, and an evolving clustering method based on TEDA (Typicality and Eccentricity Data Analytics) Framework, which uses multivariate time series in a spatiotemporal context. The model has an adaptation mechanism to deal with changes in the data distribution or concept drifts in data streams. The evolving clustering method is adjusted as the data points arrive and are processed, in an online manner. Its performance is evaluated in the application to problems of solar and wind energy forecasting as well as concept drift events. The model was developed in Python programming language using pyFTS library. To contribute to the replication of all the results, we provide all source codes in a public repository. The good results in the different experiments enable the e-MVFTS model to be used in forecasting problems with streaming data in renewable energy systems. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study introduces an evolving forecasting model e-MVFTS based on fuzzy time series and an evolving clustering method based on the TEDA framework for forecasting problems in renewable energy systems. Evaluations were conducted on solar and wind energy forecasting as well as concept drift events.