Deep and Efficient Impact Models for Edge Characterization and Control of Energy Events Network control in microgrids is an active research area driven by a steady increase in energy demand, the necessity to minimize the environmental footprint, yet achieve socioeconomic benefits and ensure sustainability. Reducing deviation of the predicted energy consumption from the actual one, softening peaks in demand and filling in the troughs, especially at times when power is more affordable and clean, present challenges for the demandside response. In this paper, we present a hierarchical energy system architecture with embedded control. This architecture pushes prediction models to edge devices and executes local control loops to address the challenge of managing demandside response locally. We employ a twostep approach: At an upper level of hierarchy, we adopt a conventional machine learning pipeline to build load prediction models using automated domainspecific feature extraction and selection. Given historical data, these models are then used to label prediction failure events that force the operator to use backup energy sources to stabilize the network. On a lower level of hierarchy, computed labels are used to train impact models realized by LSTM networks running on edge devices to infer the probability that the power consumption of the player contributes to the upper level prediction failure event. The system is evaluated on clustered and aggregated energy traces from a public data set of academic buildings. The results show the benefits of the proposed hierarchical energy system architecture in terms of impact prediction with 55% accuracy. This allows minimizing the number of prediction failure events by 11.69 % by executing targeted local control.

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.