A review of identification and monitoring methods for electric loads in commercial and residential buildings

Du, Yi; Du, Liang; Lu, Bin; Harley, R.G.; Habetler, T.G.

doi:10.1109/ecce.2010.5618423

Cited by 83 publications

(44 citation statements)

References 34 publications

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“…To classify the load signatures some of its features are responsible like current waveform (CW), active/reactive power (PQ), harmonics (HAR), instantaneous admittance waveform (IAW), instantaneous power waveform (IPW), eigenvalues (EIG), and switching transient waveform (STW) [19]. Methods to detect electrical loads through voltage, current and power measurements have been proposed by various researchers [9]. In this study the data consisting of the active power, reactive power, voltage, current and frequency signals were acquired from the meter panel.…”

Section: Load Identificationmentioning

confidence: 99%

“…PC, heater, lamp) and its operational status (e.g. active, ready, stand by) by analyzing the electric signals [9]. To classify the load signatures some of its features are responsible like current waveform (CW), active/reactive power (PQ), harmonics (HAR), instantaneous admittance waveform (IAW), instantaneous power waveform (IPW), eigenvalues (EIG), and switching transient waveform (STW) [19].…”

Section: Load Identificationmentioning

confidence: 99%

“…Thus, transient behavior of a typical load is intimately related to the physical task that the load performs [11] [12] [13]. In contrast, method based on steady state features is derived using constant or periodical signals when the electrical loads operate under a stable state [9]. In comparison with steady state signatures, the transient behavior of major appliances is found to be distinct and their features are less overlapping [4].…”

Section: Introductionmentioning

confidence: 99%

“…These methods can be broadly divided into two main categories: transient state and steady state [9] [10]. In case of transient, the primary approach of load separation is based on identification of state transitions which in most cases is done by the ON/OFF transition identification [1].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Experimental Study of Electrical Appliances Consumption Using Panel Meter Data

Nyari¹,

Castro-Sitiriche²,

Park³

2017

JPEE

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The present study uses power data from panel meter connected to the micro-grid to identify electrical energy consumption of the school appliances and their behavior in both warm-up, standby and active operation states. Before the conduction of experiments a load auditing of the school appliances was carried out by reading the rated power of each device (e.g. photocopy machine, printer, and fridge). The captures of this kind of information were essential as it provides a starting point to determine energy use of each appliance and hence simplify the process of identification. The electric parameters such as active power, reactive power and current were used to analyze the behavior of electrical appliances in all states. Experimental results show that, both active and reactive power were found to be high for old Canon photocopy machine as compared to new Canon photocopy machine. Another experiment reveals that printing one copy by using HP laser printer consumes about 700 W, while photocopying one copy with new Canon machine utilizes approximately 1100 W. This study concludes that new photocopy machine consumes more electric energy in warm-up state as compared to other states (standby and active operation states). Future work is to develop an algorithm for demand side management strategies which will enable efficient utilization of the electric energy from the micro-grid and hence bring the intended energy impact to the school.

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Section: Load Identificationmentioning

confidence: 99%

Section: Load Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Experimental Study of Electrical Appliances Consumption Using Panel Meter Data

Nyari¹,

Castro-Sitiriche²,

Park³

2017

JPEE

View full text Add to dashboard Cite

show abstract

“…At the same time, advance building energy management systems are looking beyond quantification of the energy consumption by considering fusion information [11] including for instance acoustic sensors to identify the operational state of the appliances [12], motion sensors, the frequency of the appliance used [13], as well as time and appliance usage duration [13], [14]. A more comprehensive discussion about these can be found in recent reviews, such as [15]- [17]. Moreover, new data analytic challenges arise in the context of an increasing number of smart meters, and consequently, a big volume of data, which highlights the need of more complex methods to analyze and take benefit of the fusion information [18].…”

Section: Introductionmentioning

confidence: 99%

Big IoT data mining for real-time energy disaggregation in buildings

Mocanu

Nguyen

et al. 2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question, thus paving the way for new optimized behaviors from the demand side. At the same time, the latest smart meters developments allow us to monitor in real-time the power consumption level of the home appliances, aiming at a very accurate energy disaggregation. However, due to practical constraints is infeasible in the near future to attach smart meter devices on all home appliances, which is the problem addressed herein. We propose a hybrid approach, which combines sparse smart meters with machine learning methods. Using a subset of buildings equipped with subset of smart meters we can create a database on which we train two deep learning models, i.e. Factored FourWay Conditional Restricted Boltzmann Machines (FFW-CRBMs) and Disjunctive FFW-CRBM. We show how our method may be used to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values. The proposed approach was validated on a real database, namely the Reference Energy Disaggregation Dataset. The results show that for the flexibility prediction problem solved here, Disjunctive FFW-CRBM outperforms the FFWCRBMs approach, where for classification task their capabilities are comparable.

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Acoustic and Device Feature Fusion for Load Recognition

Zoha

Gluhak

Nati

et al. 2016

Studies in Computational Intelligence

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Abstract-Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multilayer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. The highest recognition performance however is shown by support vector machines, for the device and audio recognition experiments. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only.

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A review of identification and monitoring methods for electric loads in commercial and residential buildings

Cited by 83 publications

References 34 publications

An Experimental Study of Electrical Appliances Consumption Using Panel Meter Data

An Experimental Study of Electrical Appliances Consumption Using Panel Meter Data

Big IoT data mining for real-time energy disaggregation in buildings

Acoustic and Device Feature Fusion for Load Recognition

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