Non-Intrusive Loads Monitoring Techniques for House Energy Management

Cannas, B.; Canetto, Beatrice; Carcangiu, Sara; Fanni, Alessandra; Fresi, Luca; Marceddu, Mirco; Muscas, Carlo; Porcu, Paolo; Sias, Giuliana

doi:10.1109/synergy-med.2019.8764104

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…As it can be noted, despite the performance index obtained in [7] for the synthetically generated aggregate signals was already very high, a slight improvement has been achieved. A more significant improvement in the performance index can be observed on the experimental data after the introduction of the described changes.…”

Section: Performance With Custom Datasetmentioning

confidence: 92%

“…The HEMS should identify the appliances that are active at any time, disaggregate the energy and estimate consumptions of the single device. In this work, the chosen low-frequency smart meter is a sensor belonging to a precommercial prototype [7] which provides steady-state signatures, such as real and reactive power time series. It is important that the HEMS set-up is easy to understand and interact with.…”

Section: The Home Energy Management Systemmentioning

confidence: 99%

“…In order to show the effectiveness of the improvements proposed in this paper, in Table 4 the performance for the edge detection as reported in [7], are shown. In Table 5 the event classifier performance is compared with those reported in [7].…”

Section: Performance With Custom Datasetmentioning

confidence: 99%

“…The households should follow the proposed procedure to customize the system for their homes, choosing the appliances of interest and collecting the corresponding measurements. The disaggregation algorithm is an improvement of the one proposed in [7]- [8]. The load disaggregation is performed applying a hybrid approach to power data, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Custom dataset: appliance signatures in ΔP-ΔQ plane. The overlapping signatures of hairdryer and stove are highlighted with black rectangles[7].…”

mentioning

confidence: 99%

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NILM techniques applied to a real-time monitoring system of the electricity consumption

et al. 2021

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Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

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Section: Performance With Custom Datasetmentioning

confidence: 92%

Section: The Home Energy Management Systemmentioning

confidence: 99%

Section: Performance With Custom Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Custom dataset: appliance signatures in ΔP-ΔQ plane. The overlapping signatures of hairdryer and stove are highlighted with black rectangles[7].…”

mentioning

confidence: 99%

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NILM techniques applied to a real-time monitoring system of the electricity consumption

et al. 2021

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Appliance classification on low-cost smart power outlets based on frequency feature of electric current

Nguyen

Duong

Nguyễn

2021

2021 26th IEEE Asia-Pacific Conference on Communications (APCC)

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Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring

et al. 2021

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Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

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Non-Intrusive Loads Monitoring Techniques for House Energy Management

Cited by 3 publications

References 7 publications

NILM techniques applied to a real-time monitoring system of the electricity consumption

NILM techniques applied to a real-time monitoring system of the electricity consumption

Appliance classification on low-cost smart power outlets based on frequency feature of electric current

Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring

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