Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems. (June 2021)

• Record Type: Journal Article
• Authors: Severiano, Carlos A.; Silva, Petrônio Cândido de Lima e; Weiss Cohen, Miri; Guimarães, Frederico Gadelha
• Is Part Of: Renewable energy. Volume 171(2021)
• Page Start: 764
• Page End: 783
• Publication Date: 2021-06
• Subjects: Renewable energy systems -- Multivariate time series -- Spatio-temporal forecasting -- Fuzzy time series -- Solar energy forecasting -- Wind power forecasting; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.02.117
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: 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 spatio-temporal 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. Highlights: Design of a novel evolving multivariate fuzzy time series (FTS) method. Quickly adapts to changes in data distribution or concept drifts in data streams. Multi-dimensional clusters are processed by an FTS forecasting method. Few hyper-parameters to set in both clustering and forecasting stages.