Non-Intrusive Load Monitoring Method Considering the Time-Segmented State Probability

Liu, Lina; Li, Fangshuo; Cheng, Zhijiong; Zhou, Yifei; Shen, Jie; Li, Ruichao; Xiong, Siyu

doi:10.1109/access.2022.3167132

Cited by 4 publications

(12 citation statements)

References 20 publications

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“…Liu et al's method, which is based on Affinity propagation clustering (AP) and time-segmented state probability (TSSP), was proposed as a fast working algorithm for real time disaggregation in 2021 [54]. This method was tested on the AMPds dataset and offered an average load state identification accuracy of over 96% and power decomposition accuracy of over 89%, while including all appliances.…”

Section: Traditional Methods and Shallow Algorithmsmentioning

confidence: 99%

Advances in Machine-Learning Based Disaggregation of Building Heating Loads: A Review

Lien,

Najafi,

Rajasekharan

2023

Lecture Notes in Computer Science

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This review article investigates the methods proposed for disaggregating the space heating units' load from the aggregate electricity load of commercial and residential buildings. It explores conventional approaches together with those that employ traditional machine learning, deep supervised learning and reinforcement learning. The review also outlines corresponding data requirements and examines the suitability of a commonly utilised toolkit for disaggregating heating loads from lowfrequency aggregate power measurements. It is shown that most of the proposed approaches have been applied to high-resolution measurements and that few studies have been dedicated to low-resolution aggregate loads (e.g. provided by smart meters). Furthermore, only a few methods have taken account of special considerations for heating technologies, given the corresponding governing physical phenomena. Accordingly, the recommendations for future works include adding a rigorous preprocessing step, in which features inspired by the building physics (e.g. lagged values for the ambient conditions and values that represent the correlation between heating consumption and outdoor temperature) are added to the available input feature pool. Such a pipeline may benefit from deep supervised learning or reinforcement learning methods, as these methods are shown to offer higher performance compared to traditional machine learning algorithms for load disaggregation.

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Section: Traditional Methods and Shallow Algorithmsmentioning

confidence: 99%

Advances in Machine-Learning Based Disaggregation of Building Heating Loads: A Review

Lien,

Najafi,

Rajasekharan

2023

Lecture Notes in Computer Science

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“…Non-intrusive load monitoring methods provide a costeffective solution for DNO to identify in real time the electrical location of DER [18]. These methods are focused on the disaggregation of individual loads from aggregated measurements.…”

Section: Non-intrusive Load Monitoringmentioning

confidence: 99%

“…Subsequently, a few research studies have developed NILM methods for the identification of individual DER electrical patterns at residential level. In [18] authors proposed a NILM classification method based on time-segmented state probability to identify residential loads. One week of active and reactive power from the 'AMPds' dataset [26] were used as inputs of the algorithm returning an accuracy of over 99% for the identification of an EV load signature.…”

Section: Overview Of Related Workmentioning

confidence: 99%

“…One of the main disadvantages of this research is the lack of real time identification, a characteristic required to control DER variable dynamics by DNO. The main limitation of the methods presented in [18], [27], VOLUME 11, 2023 and [28] is the lower complexity to disaggregate loads at customer level compared to the implementation of load identification techniques at distribution level.…”

Section: Overview Of Related Workmentioning

confidence: 99%

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Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

et al. 2023

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Increasing integration of distributed energy resources (DER) in the electrical network has led distribution network operators to unprecedented challenges. This issue is compounded by the lack of monitoring infrastructure on the low voltage (LV) side of distribution networks at residential and utility sides. Non-intrusive load monitoring (NILM) methods provide an opportunity to add value to conventional electric measurements and to increase the observability of LV networks for the implementation of active management network techniques and intelligent control of DER. This work proposes a novel implementation of NILM methods for the identification of DER electrical signatures from aggregated measurements taken at the LV side of a distribution transformer. The implementation evaluates three machine learning algorithms such as k Nearest Neighbours (kNN), random forest and a multilayer perceptron under 100 scenarios of DER integration. A year of minutely reported values of electric current, voltage, active power, and reactive power are used to train and test the proposed model. The F 1 scores achieved of 73% and 93% for Electrical Vehicles (EV) and rooftop photovoltaic (PV) respectively and processing times below 314 µs on an Intel Core i7-8700 machine. These results confirm the relevance of the NILM method based on low frequency electric measurements from the real-time identification of DER.INDEX TERMS Distributed energy resources (DER), non-intrusive load monitoring (NILM), low voltage networks distribution networks (LVN), random forest, supervised machine learning.The associate editor coordinating the review of this manuscript and approving it for publication was Wentao Fan .

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“…In a study by [26], a Pattern Matching (PM) methodology for electrical load disaggregation was introduced using the Affinity Propagation (AP) clustering algorithm. The method involves creating appliance consumption templates and a time-segmented state probability (TSSP) matrix for each appliance to improve load disaggregation accuracy and speed.…”

mentioning

confidence: 99%

Analysis of Variability in Electric Power Consumption: A Methodology for Setting Time-Differentiated Tariffs

Duarte,

Rosero-Garcia,

Duarte

2024

Energies

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The increasing concern for environmental conservation has spurred government initiatives towards energy efficiency. One of the key research areas in this regard is demand response, particularly focusing on differential pricing initiatives such as Time-of-Use (ToU). Differential tariffs are typically designed based on mathematical or statistical models analyzing historical electricity price and consumption data. This study proposes a methodology for identifying time intervals suitable for implementing ToU energy tariffs, achieved by analyzing electric power demand variability to estimate demand flexibility potential. The methodology transforms consumption data into variation via the coefficient of variation and, then, employs k-means data analysis techniques and the a priori algorithm. Tested with real data from smart meters in the Colombian electrical system, the methodology successfully identified time intervals with potential for establishing ToU tariffs. Additionally, no direct relationship was found between external variables such as socioeconomic level, user type, climate, and consumption variability. Finally, it was observed that user behavior concerning consumption variability could be categorized into two types of days: weekdays and non-working days.

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Non-Intrusive Load Monitoring Method Considering the Time-Segmented State Probability

Cited by 4 publications

References 20 publications

Advances in Machine-Learning Based Disaggregation of Building Heating Loads: A Review

Advances in Machine-Learning Based Disaggregation of Building Heating Loads: A Review

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

Analysis of Variability in Electric Power Consumption: A Methodology for Setting Time-Differentiated Tariffs

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