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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 demand-side 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 demand-side response locally. We employ a two-step approach: At an upper level of hierarchy, we adopt a conventional machine learning pipeline to build load prediction models using automated domain-specific 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.