NILM Applications: Literature review of learning approaches, recent developments and challenges

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F1-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

https://www.mdpi.com/1996-1073/14/9/2390/pdf

Review on Deep Neural Networks Applied to Low-Frequency NILM

Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

https://www.mdpi.com/1996-1073/14/3/767/pdf

A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first investigated, including data collection devices and public data sets. Next, event‐based and non‐event‐based NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.

/pdf/towards-trustworthy-energy-disaggregation-a-review-of-2txbiyo2.pdf

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Over the years, an enormous amount of research has been exploring Deep Neural Networks (DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for estimating the energy consumption of appliances from a single point source such as smart meters - Non-Intrusive Load Monitoring (NILM). However, most of the existing DNNs models for NILM use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. In this work, we propose UNet-NILM for multi-task appliances' state detection and power estimation, applying a multi-label learning strategy and multi-target quantile regression. The UNet-NILM is a one-dimensional CNN based on the U-Net architecture initially proposed for image segmentation. Empirical evaluation on the UK-DALE dataset suggests promising performance against traditional single-task learning.

UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM

Estimating the investments needed to achieve the Sustainable Development Goals (SDGs) is key to mobilising the financial resources to achieve them. Despite an increasing body of research to estimate the capital and operational costs towards achieving various related SDG targets individually and collectively, an overview of the total estimated investment needs at the global scale has not been conducted since the adoption of SDGs in 2015. This study provides such an overview. Estimates for investment needs are found for nine goals: SDG 2 (zero hunger), SDG 3 (good health and well-being), SDG 4 (quality education), SGD 6 (clean water and sanitation), SDG7 (access to energy), SDG 9 (infrastructure), SDG 13 (climate action), SDG 14 (life below water), and SDG 15 (life on land). The reviewed studies vary significantly in terms of applied methodology, the assumed targets that need to be achieved, and presented estimates, but overall they indicate significantly higher investment needs to achieve all covered SDGs than previous estimates suggest. For most SDGs, annual investment needs are in the order of hundreds of billion USD annually, and for SDG6 and SDG13 estimates of a trillion or more are reported.

/pdf/investment-needs-to-achieve-sdgs-an-overview-1mg6rwi4.pdf

Investment needs to achieve SDGs: An overview

Push-notifications are a communication tool leveraged by many apps to disseminate information, engage with their user base and provide a means of encouraging users to take particular actions. The nuanced intent behind a push is not always distinguishable to the end-user at moments of delivery. This work explores the text content of notifications pushed by a number of prominent apps in the marketplace over the period of 463 days. We present a new ontology that defines notification Call-to-action (CTA) labels in use today. This facilitates greater understanding behind a push and is a step towards standardisation for marketing teams. Subsequently, we then present results of a notification dataset annotated with our CTA labels and propose and evaluate a CTA text classification task, which could facilitate improved solutions for both users subscribed to, and marketers creating, push-notifications. 

https://zenodo.org/record/6630566/files/NLDB_2022___preprint.pdf

Extracting and Understanding Call-to-actions of Push-Notifications

Push-notifications, by design, attempt to grab the attention of subscribers and impart new or valuable information in a particular context. These nudges are commonly initiated by marketing teams and subsequent delivery interruptions tend to conflict with subscriber priorities and activities. In this work, we present a definition of urgency applied to notifications. We describe its value in an ontology for push-notification annotation and also evaluate a variety of classification models tasked with distinguishing urgency levels in notification text. The best model achieved an F1-score of 0.89. The proposed models have the potential to benefit subscribers by helping them better prioritize incoming notifications and also aid marketers in creating time-relevant campaigns. 

https://zenodo.org/record/7368963/files/MoMM_2022___short___preprint.pdf

Now, Later, Never: A Study of Urgency in Mobile Push-Notifications

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Papers

UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM

Investment needs to achieve SDGs: An overview

Extracting and Understanding Call-to-actions of Push-Notifications

Now, Later, Never: A Study of Urgency in Mobile Push-Notifications