Non-Intrusive Load Monitoring System Framework and Load Disaggregation Algorithms: A Survey

Sun, Mingxu; Nakoty, Francis Mawuli; Liu, Qi; Liu, Xiaodong; Yang, Yi; Shen, Tao

doi:10.1109/icamechs.2019.8861646

Cited by 17 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regularly variable, finite state or multi-state loads are those appliances with several power states when turned on, which are repeated through their operational conditions such as washing machines. Another group of loads is called continuously variable devices such as desktop computers, laptops and televisions [57], while constant loads are those appliances in continuous operation such the standby base load of electronic appliances, fans, continuously operating pumps (e.g. aquariums).…”

Section: Data Typesmentioning

confidence: 99%

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

et al. 2021

View full text Add to dashboard Cite

Section: Data Typesmentioning

confidence: 99%

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

et al. 2021

View full text Add to dashboard Cite

“…In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems (Kelly and Knottenbelt, 2015 ; Sun et al, 2019 ). Although this method provides a systematic, comprehensive, and convenient way of collecting data, it still has shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Real-time recommendations for energy-efficient appliance usage in households

et al. 2022

View full text Add to dashboard Cite

According to several studies, the most influencing factor in a household's energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house's energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2–17%.

show abstract

“…Subsequently, most of the available NILM literature is based on these highly sampled data [8]. Highly sampled data in NILM yield better energy disaggregation [9] with the larger number of appliance identifications [10] but at a cost of more complex hardware requirement, large storage demand, and huge capital investment [11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Lie

Vallès³

et al. 2021

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation. Non-intrusive load monitoring (NILM) offers many promising applications in the context of energy efficiency and conservation. Load classification is a key component of NILM that relies on different artificial intelligence techniques, e.g., machine learning. This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis. Moreover, this study also analyzes the role of input feature space dimensionality in the context of classification performance. For the above purposes, an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households.Based on the presented analysis, it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data. The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier. Furthermore, it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.

show abstract

Non-Intrusive Load Monitoring System Framework and Load Disaggregation Algorithms: A Survey

Cited by 17 publications

References 30 publications

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

Real-time recommendations for energy-efficient appliance usage in households

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Contact Info

Product

Resources

About