NoFaRe: A Non-Intrusive Facility Resource Monitoring System

Kahl, Matthias; Goebel, Christoph; Haq, Anwar Ul; Kriechbaumer, Thomas; Jacobsen, Hans‐Arno

doi:10.1007/978-3-319-25876-8_6

Cited by 12 publications

(20 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the formula, l I is the current column vector; a H is the matrix β representing the current weight coefficient column vector. a H is the unit current, which means that a H the jth column element is obtained, as shown in equation (7).…”

Section: 𝑖 (𝑡) = 𝐼 ⋅ Cos (𝑤𝑡 + 𝜃 ) + ⋯ + 𝐼 ⋅ Cos (𝑘𝑤𝑡 + 𝜃 ) + ⋯ 𝐼 = 𝛼...mentioning

confidence: 99%

Power Analysis Based on Non-intrusive Power Load Monitoring and Decomposition

Jiang¹,

Ren²,

Wang³

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Monitoring power consumption, reducing energy consumption, and achieving healthy and green development are topics of concern. In this paper, a non-invasive power load decomposition method is proposed to monitor power consumption accurately. The innovation of this paper is to propose an optimal solution method of non-invasive power coincidence decomposition and apply this method to the monitoring of power consumption. For the linear superposition estimation of voltage and current data of power equipment, the weight coefficient is calculated by using load decomposition, and the energy consumption proportion of power equipment is obtained. Finally, the experiment proves that this method has the effectiveness of power energy consumption monitoring.

show abstract

Section: 𝑖 (𝑡) = 𝐼 ⋅ Cos (𝑤𝑡 + 𝜃 ) + ⋯ + 𝐼 ⋅ Cos (𝑘𝑤𝑡 + 𝜃 ) + ⋯ 𝐼 = 𝛼...mentioning

confidence: 99%

Power Analysis Based on Non-intrusive Power Load Monitoring and Decomposition

Jiang¹,

Ren²,

Wang³

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The REDD was presented, [33][34][35] which is considered the first published available dataset used to support the research of NILM. The dataset comprises both submetered and aggregate power data from six households in the USA that were recorded for a short time (between a few weeks and a few months).…”

Section: Reference Energy Disaggregation Datasetmentioning

confidence: 99%

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Ramadan

Huang

Bamisile

et al. 2023

Energy Science & Engineering

View full text Add to dashboard Cite

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

show abstract

“…Although existing methods can perform nearly perfectly in a single dataset, there is still a lack of research on the realworld application with novel load types and few labeled samples. The types of loads commonly found in different geographic areas or in different application scenarios are often different, e.g., sandwich machines and shoe warmers in the WHITED dataset [11] are difficult to find in China; the load types in application scenarios such as light industrial workshops [11], ship systems [12], and nuclear power plants are somewhat different from those in general households. Moreover, due to the cost of data collection and manual labeling, only a very small amount of labeled data for novel loads is often available.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a total of three datasets, PLAID [16], HOUIDI [17] and WHITED [11], are used. The WHITED dataset is used to construct the meta-dataset for meta-training and testing because it has a large variety of loads and is particularly suitable for simulating novel types and few-shot settings for load identification tasks.…”

Section: Introduction Of the Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Meta-learning-based few-shot identification for novel loads

Liu¹,

Tan²,

Cong³

et al. 2023

Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022)

View full text Add to dashboard Cite

In recent years, load identification technology has received great attention as the value of real-time load-side electricity information has gradually emerged. There are several ways to precisely identify the different types of loads. However, practical situations with novel load types and little labeled data are seldom considered. For this reason, this paper proposes a few-shot identification method for novel loads based on the Model-Agnostic Meta-Learning (MAML). It uses the Adaptive Weighted Recurrence Graphs (AWRG) model as the base learner, which has the best performance in load identification, and pre-trains the model with existing data. The proposed method uses meta-training to get initial parameters that are generalized across multiple load types to improve the learning ability of the model on few-shot tasks with novel loads. Compared with transfer learning methods commonly used for generalized load identification, the results on the WHITED dataset show that the proposed method can improve the scalability of the load identification for practical applications.

show abstract

NoFaRe: A Non-Intrusive Facility Resource Monitoring System

Cited by 12 publications

References 17 publications

Power Analysis Based on Non-intrusive Power Load Monitoring and Decomposition

Power Analysis Based on Non-intrusive Power Load Monitoring and Decomposition

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Meta-learning-based few-shot identification for novel loads

Contact Info

Product

Resources

About