| Training Load Monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data |
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| 作者单位： | Civil and Environmental Engineering Department，Carnegie Mellon University |
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| 摘 要： | The growing interest in energy-efficient buildings is driving changes in investment, design, and occupant behavior. To better focus cost and resource conservation efforts, electricity consumption feedback can be used to provide motivation, guidance, and verification. Disaggregating by end-use helps both consumers and producers to identify targets for conservation. While hardware-based sub-metering is costly and labor-intensive, non-intrusive load monitoring (NILM) is capable of gathering detailed energy-use data with minimal equipment cost and installation time. However, variations in measurements between metering devices complicate the process of compiling the necessary appliance profiles. Future work involves the devel-opment of NILM algorithms using sensor fusion and detailed appliance-level data gathered from a highly-sensed house currently being constructed near Pittsburgh, Pennsylvania.
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| 关 键 词： | electricity metering feedback energy conservation non-intrusive load monitoring |
Training Load Monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data |
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| Authors: | Mario Berges Ethan Goldman H Scott Matthews Lucio Soibelman |
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| Institution: | Civil and Environmental Engineering Department, Carnegie Mellon University |
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| Abstract: | The growing interest in energy-efficient buildings is driving changes in investment, design, and occupant behavior. To better focus cost and resource conservation efforts, electricity consumption feedback can be used to provide motivation, guidance, and verification. Disaggregating by end-use helps both consumers and producers to identify targets for conservation. While hardware-based sub-metering is costly and labor-intensive, non-intrusive load monitoring (NILM) is capable of gathering detailed energy-use data with minimal equipment cost and installation time. However, variations in measurements between metering devices complicate the process of compiling the necessary appliance profiles. Future work involves the devel-opment of NILM algorithms using sensor fusion and detailed appliance-level data gathered from a highly-sensed house currently being constructed near Pittsburgh, Pennsylvania. |
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| Keywords: | electricity metering feedback energy conservation non-intrusive load monitoring |
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