Is Feedforward Learning more Efficient than Feedback Learning in Smart Meters of Electricity Consumption?


The most popular way to improve consumers’ control over their electricity cost is by providing frequent and detailed feedback with “in-home displays” (IHD). In this study, we examined alternative ways to train experimental participants to control and optimize their use of electricity by “feedforward” training to map energy consuming behaviors to costs. The participants were trained in one of four experimental conditions, one feedback (“IHD”) and three feedforward conditions before they had to control the electricity consumption in a simulated household. Results showed that one of the feedforward conditions produced somewhat higher utility and as good or better satisfaction of a monthly budget than the feedback training condition, despite never receiving any feedback about the monthly cost, but the generalization to a new budget constraint proved to be slightly poorer.

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