Mixed-Integer Nonlinear Programming for State-Based Non-Intrusive Load Monitoring

Balletti, Marco; Piccialli, Veronica; Sudoso, Antonio M.

doi:10.1109/tsg.2022.3152147

Cited by 29 publications

(22 citation statements)

References 44 publications

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“…In this direction, Basu et al [ 48 , 49 ] were the first to apply popular multi-label classification algorithms to the problem of NILM in order to detect on/off events with multiple targets. Furthermore, recent works have explored the concept of mixed-integer nonlinear programming (MINP) [ 50 , 51 , 52 ] to estimate the power consumption of many appliances simultaneously. These approaches are based on Hart’s original formulation [ 1 ] and perform disaggregation after modeling the power traces of the target appliances.…”

Section: Related Work On Multi Target Nilmmentioning

confidence: 99%

Variational Regression for Multi-Target Energy Disaggregation

Gkalinikis

Nalmpantis

Vrakas

2023

Sensors

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Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in production. Due to the high number of parameters and the different models, training and inference can be very costly. A promising solution to this problem is the design of an NILM system in which all the target appliances can be recognized by only one model. This paper suggests a novel multi-appliance power disaggregation model. The proposed architecture is a multi-target regression neural network consisting of two main parts. The first part is a variational encoder with convolutional layers, and the second part has multiple regression heads which share the encoder’s parameters. Considering the total consumption of an installation, the multi-regressor outputs the individual consumption of all the target appliances simultaneously. The experimental setup includes a comparative analysis against other multi- and single-target state-of-the-art models.

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Section: Related Work On Multi Target Nilmmentioning

confidence: 99%

Variational Regression for Multi-Target Energy Disaggregation

Gkalinikis

Nalmpantis

Vrakas

2023

Sensors

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“…Since this optimization problem is a nonlinear integer programming problem, 48 it is important to constrain the integration bus number of DG to be an integer and the capacity to be in line with the actual situation, that is, the total capacity of DG is greater than 0 and less than the total load. The details are as follows:

({, \begin{array}{l} 0 \leq \sum_{i} P_{{italicDG}_{i}} x_{i} \leq P_{load} \\ x_{i} \in ({}, 0, 1) i \in Ω_{in} \end{array})

where

P_{DGi}

represents the DG active power in bus i ,

P_{load}

is the load power.…”

Section: Problem Formulationmentioning

confidence: 99%

A line loss reduction optimization for renewable energy‐based distribution networks using a probabilistic approach

Jiang,

Han,

et al. 2023

Circuit Theory & Apps

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SummaryThe large integration of distributed generation (DG) and electric vehicles (EVs) facilitates daily life and reduces carbon emissions. However, it brings problems such as increased uncertainty and power electronic permeability to the distribution network. In this context, it is important to solve the line loss optimization problem as closely as possible to the real situation. Therefore, this paper solves the line loss optimization problem based on a probabilistic model of three‐phase unbalanced modern power electrical distribution network. Firstly, the paper proposes a frequency domain model of the distribution network and the optimization problem model. Then, to deal with the uncertainty of the system, the point estimation method (PEM) and Monte Carlo Simulation (MCS) are used. Finally, this paper proposes two optimization schemes considering the power quality when single and multiple DGs are integrated. The simulation results demonstrate that the PEM using the Gram–Charlier series expansion of the fourth‐order statistical moments to estimate the probability density function (PDF) is significantly less time consuming than the traditional MCS method while maintaining accuracy. The comparative analysis reveals that under the optimization scheme with single DG integration, the expected value of system line loss and expected line loss rate are reduced from 283.95 kW to 218.96 kW (22.89% reduction) and from 13.44% to 10.68%, respectively. Under the dual DG integrated optimization scheme, the expected value of line losses and expected line loss rate are reduced from 283.95 kW to 208.88 kW (26.4% reduction) and from 13.44% to 10.25%, respectively. However, after taking the uncertainty into account, the operational reliability of the system has been enhanced under different scenarios. In addition, it is found that the optimization approach incorporating multiple DG units can effectively mitigate the system line loss under diverse operational scenarios while enhancing the system's DG integration capability.

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“…Providing residents with fine-grained energy usage information through Non-Intrusive load decomposition (NILD) has become an important way to effectively reduce energy waste [2]. NILD analyzes the operating status of each appliance from the total load data of residential customers which can guide customers to improve their appliance usage habits and reduce household electricity consumption [3][4][5]. At the same time, it helps the grid to build a more accurate model of household electricity consumption, enabling monitoring, control, management, and friendly interaction with electricity-using devices [6].…”

Section: Introductionmentioning

confidence: 99%

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet

et al. 2023

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In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-trained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function to solve the problem of data imbalance in V-I trajectory identification. All parameters are retrained to extract features from the color V-I trajectory images for device classification. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets. The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 minutes compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification.

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Mixed-Integer Nonlinear Programming for State-Based Non-Intrusive Load Monitoring

Cited by 29 publications

References 44 publications

Variational Regression for Multi-Target Energy Disaggregation

Variational Regression for Multi-Target Energy Disaggregation

A line loss reduction optimization for renewable energy‐based distribution networks using a probabilistic approach

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet

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