A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings

TL;DR

This paper compares adaptive fuzzy inference algorithms for real-time energy prediction in urban buildings, highlighting McFIS's superior performance and lower complexity based on empirical data analysis.

Contribution

It provides a comprehensive comparison of McFIS, SAFIS, and ETS algorithms for energy prediction, emphasizing the advantages of McFIS in accuracy and complexity.

Findings

McFIS achieves lower RMSE and NDEI than ETS and SAFIS.

Statistical analysis confirms the significance of performance differences.

McFIS demonstrates promising results with reduced architectural complexity.

Abstract

This investigation aims to study different adaptive fuzzy inference algorithms capable of real-time sequential learning and prediction of time-series data. A brief qualitative description of these algorithms namely meta-cognitive fuzzy inference system (McFIS), sequential adaptive fuzzy inference system (SAFIS) and evolving Takagi-Sugeno (ETS) model provide a comprehensive comparison of their working principle, especially their unique characteristics are discussed. These algorithms are then simulated with dataset collected at one of the academic buildings at Nanyang Technological University, Singapore. The performance are compared by means of the root mean squared error (RMSE) and non-destructive error index (NDEI) of the predicted output. Analysis shows that McFIS shows promising results either with lower RMSE and NDEI or with lower architectural complexity over ETS and SAFIS. Statistical Analysis also reveals the significance of the outcome of these algorithms.