Optimal Detection and Error Exponents for Hidden Semi-Markov Models

Bajović, Dragana; He, Kanghang; Stanković, Lina; Vukobratović, Dejan

doi:10.1109/jstsp.2018.2851506

Cited by 11 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hidden Markov models (HMMs) and various extensions of them are advocated in order to explore the possible combinations among the different appliances’ state sequences [ 14 , 29 , 30 , 31 ]. In this light, HMMs are state-based, so the studied appliances should have discrete states in their signatures [ 32 ].…”

Section: A Brief Nilm Literature Reviewmentioning

confidence: 99%

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

Kaselimi

Protopapadakis

Voulodimos

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: A Brief Nilm Literature Reviewmentioning

confidence: 99%

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

Kaselimi

Protopapadakis

Voulodimos

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…On the contrary, state-based NILM approaches require an a-priori knowledge or a large training dataset, to achieve good performance [19]. Hidden Markov models (HMM) and various extensions of this model were proposed to examine the different combinations of appliances' state sequences [20]- [23]. In this light, HMMs are state-based and so the studied appliances should have discrete states in their signatures [5].…”

Section: Related Work and Paper Contributionmentioning

confidence: 99%

Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation

et al. 2019

View full text Add to dashboard Cite

Power consumption signals of household appliances are characterized by randomly occurring events (e.g. switch-on events), making timeseries modeling a demanding process. In this paper, we propose a convolutional neural network (CNN)-based architecture with inputs and outputs formed as data sequences taking into consideration an appliance's previous states for better estimation of its current state. Furthermore, the proposed model endows CNN models with a recurrent property in order to better capture energy signal interdependencies. Using a multi-channel CNN architecture fed with additional variables related to power consumption (current, reactive, and apparent power), additionally to active power, overall performance, robustness to noise and convergence times are improved. The experimental results prove the proposed method's superiority compared to the current state of the art. INDEX TERMS Convolutional neural network (CNN), deep learning, energy disaggregation, load monitoring, NILM, power, recurrent neural networks, tapped delay line, sequence to sequence modeling.

show abstract

“…Another recent work makes use of Activities of Daily Livings (ADL) to build a classification method using a deep learning architecture for sensing the activation of specific major household devices [3].The proposed approach is tested on the UK-DALE [12] dataset and the results confirm the effectiveness of the solution. Various adaptations of the Hidden Markov Models (HMM) have been evaluated for forecasting the possible combinations of operation state of residential appliances [13]- [14]. The study of [15] used a Factorial Hidden Markov Models (FHMM) to determine the device-specific load models considering the overall power readings which obtained a disaggregation efficiency of 90% and 80% respectively for type I and type II devices.…”

Section: Related Workmentioning

confidence: 99%