Preliminary Sensitivity Analysis of Combinatorial Optimization (CO) for NILM Applications: Effect of the Meter Accuracy

Berrettoni, G.; Bourelly, C.; Capriglione, Domenico; Ferrigno, Luigi; Miele, Gianfranco

doi:10.1109/rtsi50628.2021.9597362

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Of course, in reality, between different 'operating states' there are transients that can lead to more or less marked variations in electrical quantities. As regards d), furthermore, these variations can also be related to the natural duty cycle of the equipment or be due to the uncertainty of the monitoring system used [10]. eLAMI reports this variability thanks to the mathematical model for generating absorption profiles implemented.…”

Section: A Requirementsmentioning

confidence: 99%

“…More in depth, the above-mentioned techniques are generally based on machine learning algorithms or on optimization techniques exploiting electrical parameters related to the electrical signature of a load and/or Power Quality parameters. For example, considering the development of NILM solutions that is involving many researchers in the scientific community, some algorithms are based on the use of Markovian models (HMM) and their variants [5] [6] [7], while others prefer signal processing techniques using graphs (Graph Signal Processing) [8] [9] or Combinatorial Optimization [10]. In recent years, other Machine Learning techniques have also been applied for nonintrusive monitoring, such as Multilayer Perceptron (MLP) [11], Convolutional Neural Networks (CNN) [12], Deep Learning [13] [14], Recurrent Neural Network (RNN) [15], Extreme Learning Machine [16] and Bayes Classifier [17].…”

Section: Introductionmentioning

confidence: 99%

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eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications

et al. 2022

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Smart Energy Applications are particularly impacting, especially due to energy resource scarcity and its high associated costs. A smart management of the energy consumption derives both from the user lifestyle, in terms of efficient and responsible behaviors, and from automatic algorithms that control and counteract energy waste and inefficient management. Focusing the attention on the latter, the development of methodologies and well-working techniques to monitor and optimize consumptions often requires an important effort in long measurement campaigns to get raw data to work with. Whenever this should be too much expensive or proper instrumentation unavailable, public datasets could solve the problem. Current literature review on the dataset availability showed a large presence of information, especially related to electrical energy consumptions. Nevertheless, several limitations affect them, from the low number of calculated electrical parameters (i.e. 4-5 in the most of cases), to short analysis periods, passing by the lack of detailed frequency domain information or poor consumption habit transitions analysis. Accordingly, this work aims to overcome current dataset limitations, by proposing a real-measurement based simulated dataset, extracting more than 400 discriminative electrical parameters on 36 different home appliances, discussing preliminary acquisition set-ups, simulation process, extracted electrical parameters and examples of applicability to smart energy applications. To provide a data quality index, a validation procedure has also been carried out, showing how simulated data match real acquisition with a reference measurement instrument. The produced dataset is available for downloading and analysis in public free access and its repository link is provided in the reference section.

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Section: A Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications

et al. 2022

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“…In general, NILM can be described as a combinatorial optimization (CO) problem where the purpose is to find the optimal set of operational states that better reconstructs the total power consumption of the house. Some recent studies tried to solve NILM as a CO problem (Ajani et al 2022;Berrettoni et al 2021). However, this approach is rarely applicable in the real-world since it requires to known the exact power consumption of every appliance present in the house beforehand.…”

Section: Related Workmentioning

confidence: 99%

Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances

et al. 2022

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Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world.

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“…Optimal combination seeks to match observed power measurements with all possible device power signal combinations [21]. In [22], the Particle Swarm Optimization (PSO) method was upgraded by introducing temporal probability and integrated with many optimal combination algorithms as the recognition classifier of NILM to obtain good classification results.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Monitoring Based on Swin-Transformer with Adaptive Scaling Recurrence Plot

et al. 2022

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Non-Intrusive Load Monitoring (NILM) is an effective energy consumption analysis technology, which just requires voltage and current signals on the user bus. This non-invasive monitoring approach can clarify the working state of multiple loads in the building with fewer sensing devices, thus reducing the cost of energy consumption monitoring. In this paper, an NILM method combining adaptive Recurrence Plot (RP) feature extraction and deep-learning-based image recognition is proposed. Firstly, the time-series signal of current is transformed into a threshold-free RP in phase space to obtain the image features. The Euclidean norm in threshold-free RP is scaled exponentially according to the voltage and current correlation to reflect the working characteristics of different loads adaptively. Afterwards, the obtained adaptive RP features can be mapped into images using the corresponding pixel value. In the load identification stage, an advanced computer vision deep network, Hierarchical Vision Transformer using Shifted Windows (Swin-Transformer), is applied to identify the adaptive RP images. The proposed solution is extensively verified by four real, measured load signal datasets, including industrial and household power situations, covering single-phase and three-phase electrical signals. The numerical results demonstrate that the proposed NILM method based on the adaptive RP can effectively improve the accuracy of load detection.

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Preliminary Sensitivity Analysis of Combinatorial Optimization (CO) for NILM Applications: Effect of the Meter Accuracy

Cited by 6 publications

References 13 publications

eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications

eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications

Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances

Non-Intrusive Load Monitoring Based on Swin-Transformer with Adaptive Scaling Recurrence Plot

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