A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers

Abstract Big Data advancements motivate researchers to develop and improve intelligent models to deal efficiently and effectively with data. In this scenario, time series forecasting obtains even more attention. The literature demonstrated the better performance of such models in this subject. Forecasting is widely used in strategic planning to support decision-making, providing competitive differential to organizations. In this paper, a novel rule-based evolving Fuzzy System is proposed for time series forecasting. This is a robust model able to develop and update its structure in unknown environments, capture dynamics and changes of streams, and produce accurate results even when dealing with complex data. The introduced model implements the distance correlation to improve the rules’ quality by reducing their standard deviation. The model is evaluated using two synthetic datasets: the Mackey–Glass time-series and the nonlinear dynamic system identification. And finally, the introduced system is implemented to predict the hot spot temperature using three datasets from a real power transformer. Hot spot monitoring is necessary to maximize the load capacity and the lifespan of power transformers. The proposed method is evaluated in terms of root-mean-square error, non-dimensional index error, mean absolute error, runtime, and the number of final rules. The results are compared with traditional forecasting models and with some related state-of-the-art rule-based evolving Fuzzy Systems. The new evolving Fuzzy System outperformed the compared models for the Mackey–Glass time-series and the power transformers datasets concerning the errors. A statistical test comprised the superior performance of the introduced model. The algorithm also obtained a competitive execution time and number of final rules. The results demonstrate the high level of autonomy and adaptation of the model to predict accurately complex and non-stationary data. Seeing the importance of accurate models to deal with data to support decision-making, the results suggest the model’s implementation as a forecasting tool in strategic planning.