Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clusteringbased analysis of energy consumption to categorize the consumersxe2x80x99 electricity usage into different levels. First, a deep autoencoder that transfers the lowdimensional energy consumption data to highlevel representations was trained. Second, the highlevel representations were fed into an adaptive selforganizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.

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.