{"id":58337,"date":"2024-02-28T17:57:51","date_gmt":"2024-02-28T16:57:51","guid":{"rendered":"https:\/\/www.smart-impulse.com\/?p=58337"},"modified":"2024-02-28T17:57:51","modified_gmt":"2024-02-28T16:57:51","slug":"nilm-focus","status":"publish","type":"post","link":"https:\/\/www.smart-impulse.com\/en\/nilm-focus\/","title":{"rendered":"NILM – Focus on Non-Intrusive Monitoring"},"content":{"rendered":"

What is Non-Intrusive Monitoring?<\/span><\/strong><\/h2>\n

The principle of non-intrusive monitoring<\/span><\/a>, also known as NILM or NIALM<\/strong> (Non Intrusive (Appliance) Load Monitoring), involves measuring aggregated consumption data and using intelligent algorithms to identify electrical consumption by usage.<\/p>\n

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Studied in laboratories since the 1980s, these technologies have been commercialised by a few innovative companies globally since 2010. Among these companies, some (including Smart Impulse<\/a><\/span>) have brought these technologies to maturity and industrialised their deployment. As a result, these solutions are now rapidly spreading due to their ease of implementation and the clarity of the data they provide for addressing energy management challenges.<\/p>\n

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Today there are two technological families for disaggregating the load curve: one is based on the analysis of events (switching on\/off) on the load curve, while the other focuses on a detailed analysis of the shape of the electrical signal distorted by consuming equipment. These two technologies are presented in more detail in the paragraph: ‘The different NILM technologies<\/strong>‘.<\/p>\n

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What is NILM used for?<\/span><\/strong><\/h2>\n

These technologies address many use cases, from identifying energy savings or consumption drifts to assisting with environmental certifications<\/strong> (HQE, BREEAM, LEED, etc.), as well as reporting, user awareness, or understanding the life of the building.<\/p>\n

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The Different NILM Technologies<\/span><\/strong><\/h2>\n

\"NILMAs previously seen, today two main approaches to load curve disaggregation are used. These two approaches differ in the precision of the input data used, the way it is processed, and the nature of the information returned.<\/p>\n

The first approach is based on the detection of events (switching on\/off) observed on the load curve at a frequency of about one second. This is referred to as low-frequency technology<\/strong>.<\/p>\n

The second approach is based on an analysis of the shape of the electrical signal. It uses continuous measurement of the electrical current shape as input data to finely analyse the “distortions” caused by the consuming equipment on the network. This identification of distortions, or electrical signatures, is obtained through a very high-frequency measurement of the electrical signal. In this case, it is referred to as high-frequency technology<\/strong>.<\/p>\n

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