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In the push to develop smart energy systems, designers have increasingly focused on systems that measure and predict user behavior to effect optimal energy consumption. While such focus is an important component in these systems' success, designers have paid substantially less attention to the people on the other side of the energy system loop-the supervisors of power generation processes. Smart energy systems that leverage pervasive computing could add to these supervisory control operators' workload. They'll have to predict possible power plant load and production changes caused by environmental and plant events, as well as dynamic system adaptation in response to consumer behaviors. Contrary to many assumptions, inserting more automation, including distributed sensors and algorithms to postprocess data, won't necessarily reduce operators' workload or improve system performance.