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This investigation aims to study different adaptive fuzzy inferencealgorithms capable of real-time sequential learning and prediction oftime-series data. A brief qualitative description of these algorithms namelymeta-cognitive fuzzy inference system (McFIS), sequential adaptive fuzzyinference system (SAFIS) and evolving Takagi-Sugeno (ETS) model provide acomprehensive comparison of their working principle, especially their uniquecharacteristics are discussed. These algorithms are then simulated with datasetcollected 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. Analysisshows that McFIS shows promising results either with lower RMSE and NDEI orwith lower architectural complexity over ETS and SAFIS. Statistical Analysisalso reveals the significance of the outcome of these algorithms.

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