  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.
