The exponential increase in load demand of the residential sector results in decreased quality of service and increased demand-supply gap in the electricity market. To tackle these concerns, utilities need to manage the demand response (DR) of the connected loads. However, most of the existing DR management schemes have not explored the issue of reducing peak load while taking consumer constraints into account such as user comfort and willingness to participate. To address this issue, a new data analytical DR management scheme for residential load is proposed in this paper with an aim to reduce the peak load demand. The proposed scheme is based on the analysis of consumers' consumption data gathered from smart homes for which factors such as appliance adjustment factor, appliance priority index, appliance curtailment priority, etc., have been developed. Based on these factors, different algorithms with respect to consumer's and utility's perspective have been proposed to take DR decisions in the peak load scenario. Moreover, an incentive scheme is also presented to increase the consumers' participation in the proposed scheme. The proposed scheme is tested on the dataset gathered from PJM and Open Energy Information. The results obtained show that it efficiently reduces the peak load at the grid to a great extent. Moreover, it also increases the savings of the consumers by reducing their overall electricity bills.
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