Abstract
Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics.
| Original language | English |
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| DOIs | |
| Publication status | Published - 3 Aug 2023 |
| Event | IEEE 23rd International Conference on Environment and Electrical Engineering (EEEIC) - Madrid, Spain Duration: 6 Jun 2023 → 9 Jun 2023 https://www.aconf.org/conf_187075.2023_IEEE_International_Conference_on_Environment_and_Electrical_Engineering_and_2023_IEEE_Industrial_and_Commercial_Power_Systems_Europe.html |
Conference
| Conference | IEEE 23rd International Conference on Environment and Electrical Engineering (EEEIC) |
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| Country/Territory | Spain |
| City | Madrid |
| Period | 6/06/23 → 9/06/23 |
| Other | The aim of the conferences is to give the opportunity of a genuine and constructive dialogue among participants on the hot-topics and far-reaching challenges that engineers and scientists are called to face in the present days. The conference is so a precious chance to discuss recent developments and practical applications in crucial areas, such as sustainable and renewable energy production, energy storage, smart grids, microgrids, energy communities, smart buildings, energy conversion, sustainable transport systems, EMC control in lightning and grounding systems, novel materials and nanotechnology. |
| Internet address |
Keywords
- Smart Grids, NILM, Deep Learning, Energy Management.
