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Demand response offers financial incentives for consumers who are willing to relocate some or all of their electricity demand during peak periods. In price based demand response programs, utility companies can use energy pricing as an effective control signal to reschedule electricity demand from peak hours to off-peak periods. A real-time (RT) residential time-of-use pricing system with closed-loop consumer feedback is proposed in this paper. The proposed system aims to maximize the social welfare by lowering the cost of energy for both the utility and the consumer. A cloud application is developed using a support vector machine (SVM) to forecast individual household participating in the system. An aggregator is then utilized to accumulate the predicted demand for a local micro-grid to determine peak demand. The corresponding hourly RT electricity prices are estimated based on the historical RT price elasticity and amount of peak demand needed to be rescheduled. This information is sent to consumers to affect their electricity usage during peak hours. Using the Green Button Application Programming Interface (API), the consumer's response to the RT energy price is observed through meter readings.

Abstract. With the recent advances in the field of electroencephalography (EEG), researchers have been able to detect and predict cognitive states and patterns based on electromagnetic signals emitted by the brain with greater precision than ever before. EEG has become a viable means of implementing brain-computer-interfaces (BCIs), which translate brain signals into machine commands. We have developed a system with reconfigurable hardware and open architecture component based software to enable multi-sensor physiological signal monitoring. The goal of this research is to study the feasibility of using the system that can facilitate monitoring of visual attention EEG brain signal.

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