Abstract
Responsible use of energy resources, conserving environment and improving well-being of the society are the major goals towards developing smart sustainable cities, and energy management is the inherent part of the smart sustainable cities programme. Due to high demand of energy in various sectors, energy management has become essential to utilize the available energy efficiently and understand energy usage behavior of the users in order to avoid possible energy wastage. In recent years, non-intrusive load monitoring (NILM) technique has become popular and emerging approach to monitor events and energy consumption of appliances/electrical utilities in buildings using single energy meter. The information about energy consumption at appliance level would help consumers to understand their appliance usage behavior and take necessary steps for reducing energy consumption. The recent advancements in smart metering and internet of things (IoT) have made significant progress in real time implementation of non-intrusive load monitoring (NILM) algorithms in residential and industrial environments. The motivation of this presentation is to address the challenges towards developing more accurate NILM systems and its future perspectives for effective energy management in buildings. NILM can also be used for anomaly detection, predictive maintenance, load forecasting, and energy disaggregation of distributed energy resources in buildings. Therefore, creating awareness about the importance of NILM and incorporating its potential applications as the features of NILM system would help achieve responsible energy utilization in residential/industrial environments and conserve energy consumption towards developing smart sustainable cities for the society.
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CITATION STYLE
Rajendiran, G. (2022). Non-intrusive load monitoring: A promising path to the society for responsible energy utilization and sustainability. In e-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems (pp. 594–596). Association for Computing Machinery, Inc. https://doi.org/10.1145/3538637.3539635
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