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Paper Type
ERF
Description
Modern power grids are undergoing many changes to meet the growing energy demand. A major limitation of traditional power grids is that they only allow for understanding the overall household’s total consumption, which is typically measured with high latency over the entire billing period. Smart grids and smart meters, on the other hand, enable access to timely household data. When such grids are equipped with the ability to read device-level data then they can be used to provide deeper analytic insights into energy utilization patterns at a very granular level. In this study, we propose using Dynamic Time Warping as a distance metric for clustering to examine consumers’ device-level energy consumption for four different geographic regions. Additional insight into device-level usage patterns for consumers is likely to lead us to develop persuasive IS systems that can improve energy efficiency and bring benefits to both power suppliers and power consumers.
Paper Number
1403
Recommended Citation
Lee, Jonathan and Gupta, Ashish, "Analyzing Energy Consumption of Appliances in Smart Grids" (2023). AMCIS 2023 Proceedings. 5.
https://aisel.aisnet.org/amcis2023/sig_green/sig_green/5
Analyzing Energy Consumption of Appliances in Smart Grids
Modern power grids are undergoing many changes to meet the growing energy demand. A major limitation of traditional power grids is that they only allow for understanding the overall household’s total consumption, which is typically measured with high latency over the entire billing period. Smart grids and smart meters, on the other hand, enable access to timely household data. When such grids are equipped with the ability to read device-level data then they can be used to provide deeper analytic insights into energy utilization patterns at a very granular level. In this study, we propose using Dynamic Time Warping as a distance metric for clustering to examine consumers’ device-level energy consumption for four different geographic regions. Additional insight into device-level usage patterns for consumers is likely to lead us to develop persuasive IS systems that can improve energy efficiency and bring benefits to both power suppliers and power consumers.
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