Load disaggregation in non-intrusive load monitoring based on random forest optimized by particle swarm optimization

Gong, Feixiang; Jiang, Linru; Li, Hao; Jiang, Lin; Yin, Bo

doi:10.1109/ei2.2017.8245609

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…• Optimisation methods such as Optimized Bird Swarm Algorithm or OBSA [8], genetic algorithms [9] and Particle Swarm Optimization or PSO [10], among others.…”

Section: Related Workmentioning

confidence: 99%

DSUALMH- A new high-resolution dataset for NILM

Rodríguez-Navarro¹,

Alcayde²,

Isanbaev³

et al. 2024

RE&PQJ

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The optimisation of energy consumption requires a reasonably accurate measurement, so an appropriate and advanced monitoring system of the relevant electrical variables in the electrical installations is of paramount importance. In this context, interoperable and highly configurable devices play a crucial role. A clear example is the OpenZMeter (OZM) which is an open source, open hardware, multi-purpose precision smart meter that can measure a wide range of electrical variables at a high sampling rate and provide processed data on power quality. The aim of this work is to show the use and possible applications of the new high sampling frequency data provided by the OZM device, which are much richer and more accurate than those obtained with other low-cost electrical meters. For this purpose, the opensource tool NILMTK has been used and adapted. Likewise, the use of two of the best known and most widely used algorithms such as Combinatorial Optimisation (CO) and the Factorial Hidden Markov Model (FHMM) has been considered, analysing the results obtained in the experimental study and offering a detailed comparison of the performance of the two different disaggregation algorithms using metrics for the different cases, as well as the incorporation of transients, and the comparison with other public Datasets.

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“…• Optimisation methods such as Optimized Bird Swarm Algorithm or OBSA [8], genetic algorithms [9] and Particle Swarm Optimization or PSO [10], among others.…”

Section: Related Workmentioning

confidence: 99%

DSUALMH- A new high-resolution dataset for NILM

Rodríguez-Navarro¹,

Alcayde²,

Isanbaev³

et al. 2024

RE&PQJ

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“…is a straightforward way to solve the load disaggregation task, which is to compare the extracted load signatures with the features in database and obtain the minimal error by optimization strategy. In recent research, optimization model is improved by various methods, such as optimized support vector regression [6], optimized bird swarm algorithm [7], genetic algorithm [8], and particle swarm optimization method [9]. The supervised disaggregation methods require existing specific information of devices and need initial training phase.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Identification Method Based on Improved KM Algorithm

Xiao¹,

He³

et al. 2019

IEEE Access

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To achieve load disaggregation in non-intrusive load monitoring (NILM) system, a load event matching method based on graph theory is proposed, which is built on the improved Kuhn-Munkras algorithm. In this method, firstly, an adaptive fitting method using time window is applied to detect the load whether it is switched on and/or off. Particularly, to avoid the fluctuation of load signatures, the kernel density estimation is then built by a number of the independent features of the load switching on, including the active and reactive power signatures. The distribution of load signatures is thereby obtained, allowing the load event to be classified by its features. The load matching, which is based on the improved KM algorithm, is then utilized to resolve the matrix formed by the matching probability of the load event. Similarly, load identification can also be realized by matching the features of events with the signatures in the database. Finally, the experimental results using datasets of our lab and REDD demonstrate that the proposed method can obtain the desirable result for load event matching, and promote the performance in load identification.INDEX TERMS Non-intrusive, load event, load matching, KM Algorithm, load identification.

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“…Methods of optimization: these methods use optimization techniques to conduct load disaggregation. Examples of these methods are Vector Support Machines (SVMs) [8], Bird Swarm Algorithms (BSAs) [9], Genetic Algorithms [10], and Particle Swarm Optimization (PSO) [11], among others; • Supervised methods: these methods use tagged training datasets where individual exposures are known. Some examples of supervised methods are Bayesian [12], Vector Support Machines (SVM) [13], the algorithm of Discriminative Disaggregation Sparse Coding (DDSC) [14], and Artificial Neural Networks (ANN) [15], as well as their extensions; • Unsupervised methods: use clustering techniques and statistical models for pattern recognition and load segmentation.…”

mentioning

confidence: 99%

Development and Application of an Open Power Meter Suitable for NILM

Rodríguez-Navarro,

Portillo,

Martínez

et al. 2023

Inventions

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In the context of the global energy sector’s increasing reliance on fossil fuels and escalating environmental concerns, there is an urgent need for advancements in energy monitoring and optimization. Addressing this challenge, the present study introduces the Open Multi Power Meter, a novel open hardware solution designed for efficient and precise electrical measurements. This device is engineered around a single microcontroller architecture, featuring a comprehensive suite of measurement modules interconnected via an RS485 bus, which ensures high accuracy and scalability. A significant aspect of this development is the integration with the Non-Intrusive Load Monitoring Toolkit, which utilizes advanced algorithms for energy disaggregation, including Combinatorial Optimization and the Finite Hidden Markov Model. Comparative analyses were performed using public datasets alongside commercial and open hardware monitors to validate the design and capabilities of this device. These studies demonstrate the device’s notable effectiveness, characterized by its simplicity, flexibility, and adaptability in various energy monitoring scenarios. The introduction of this cost-effective and scalable tool marks a contribution to the field of energy research, enhancing energy efficiency practices. This research provides a practical solution for energy management and opens advancements in the field, highlighting its potential impact on academic research and real-world applications.

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Load disaggregation in non-intrusive load monitoring based on random forest optimized by particle swarm optimization

Cited by 7 publications

References 12 publications

DSUALMH- A new high-resolution dataset for NILM

DSUALMH- A new high-resolution dataset for NILM

Non-Intrusive Load Identification Method Based on Improved KM Algorithm

Development and Application of an Open Power Meter Suitable for NILM

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