Electricity Consumption Forecasting of Buildings Using Hierarchical ANFIS and GRA Because of the rise of environmental awareness, controlling and monitoring the electricity consumption become significant. The accuracy of the prediction of electricity consumption can directly influence the efficiency of power management. If the usage status of electricity can be predicted, it will be easy to discover if there is any unusual electricity consumption. The choice of suitable models or mathematic methods will be the essential of all. Adaptive networkbased fuzzy inference system combines the concept of fuzzy and neural networks. It reserves the interpretability of fuzzy inference system and the learning ability of neural networks. We applied adaptive networkbased fuzzy inference system (ANFIS) with hierarchical structure on electricity consumption prediction and grey relational analysis (GRA) on the influence of each input factors. The result showed that hierarchical ANFIS did achieve the purpose we set and GRA can effectively evaluate the magnitude of relation between factors and specific output.

Shortterm Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management The electricity consumption profile of buildings are different from the typical load curves that represent the electricity consumption of large systems at the national or regional level. The electricity demand in buildings is many times lower than the region or nationwide demands. It is also much more volatile and stochastic, meaning that the conventional tools are not effective enough for straightforward application at a building demand level. In this paper, an integrated approach consisting of HilbertHuang Transform (HHT), Regrouping Particle Swarm Optimization (RegPSO) and Adaptive NeuroFuzzy Inference System (ANFIS) is devised for 24hahead prediction of electric power consumption in buildings. The forecasts are used as input information for smart decisions of distributed energy management systems that control the optimal bidding and scheduling of energy resources for building energy communities. The effectiveness of the proposed forecasting approach is demonstrated using actual electricity demand data from various buildings in the Otaniemi area of Espoo, Finland. The prediction performance of the proposed approach for various building types (energy customer clusters), has been examined and statistical comparisons are presented. The prediction results are presented for future days with a onehour time interval. The validation results demonstrate that the approach is able to forecast the buildingsxe2x80x99 electricity demand with smaller error, outperforming five other approaches, and in reasonably short computation times.