A Review of Identification and Monitoring Methods for ...

A Review of Identification and Monitoring Methods for Electric Loads in Commercial and Residential Buildings Yi Du†, Liang Du†, Bin Lu‡, Ronald Harley†, and Thomas Habetler† †Georgia Institute of Technology Department of Electrical and Computer Engineering 777 Atlantic Dr. NW Atlanta, GA 30332

‡Eaton Corporation Innovation Center 4201 N. 27th St Milwaukee, WI 53216

Abstract – Electricity consumption in commercial and residential buildings account for around 70 percent of the total electricity consumption in the United States. Through advanced load identification and management technologies, electric energy consumption and carbon emissions in buildings can be reduced by providing fine management of energy usage in an efficient way. This paper gives a state-of-the-art review of monitoring and identification methods for electric loads in commercial and residential buildings, compares their applicability and accuracy on different kinds of loads, and updates possible direction for future research.

This paper first reviews the distribution of energy consumption of electric loads in commercial and residential buildings and identifies major loads based on energy consumption. These loads should be taken into account for identification and monitoring with high priority. Section III introduces the general framework for load identification and Section IV reviews both the steady state based and transient state based methods for identifying harmonic electric sources. Also, advanced methods such as optimization methods and intelligent learning methods are also discussed. Section V summarizes this paper and suggests the direction for future researches.

Index Terms—Energy efficiency, High energy efficiency buildings, Load identification, Load monitoring.

I.

II.

INTRODUCTION

Electricity consumption in commercial and residential sectors accounts for 70 percent of the total electricity consumption in the United States [1]. It can be reduced through new construction designs, technologies and materials. The heating and cooling loads can be drastically lowered by adding insulation, high-efficiency windows and natural ventilation at energy-efficient homes. However, electric loads, especially miscellaneous electrical loads (MELs), represents a significant portion of the total building energy consumptions and this percentage is likely to increase in the future [2]. Accurate and reliable electric load identification and monitoring provide critical information that enables buildings to effectively manage the electric loads. For example, some electric loads, such as TVs and monitors, could be turned off automatically when no occupants are presented. It can also help to estimate the energy consumption of end uses [3], or to detect equipment degradation based on load monitoring system [4]. Nowadays, the majority of loads connected to a building remain unidentified due to the lack of intelligent load identification and monitoring capability, as well as the communication capability between the loads and the building management system. The original idea for load monitoring and identification has been proposed in the 1980s [5]. Over the past twenty years, many methods have been proposed by researchers. By identifying the loads types and power utilization from the current and voltage signatures, these methods can provide useful information for load management system and therefore improve the system efficiency.

ELECTRIC LOADS IN COMMERCIAL AND RESIDENTIAL BUILDINGS

The electric price in all economic sectors rose sharply over the decades. Therefore, there is a great need from the customers to reduce the electric energy spending and increase system efficiency. The following sections will discuss and compare the loads in commercial and residential buildings based on energy consumption. A.

Electric Loads in Commercial Sector Electricity consumption in commercial sectors accounts for 35 percent of the total electricity consumption in the U.S., and it is expected to encounter an increase of 40% from 2010 to 2030 [6]. Lighting, space heating and cooling, water heating, ventilation and refrigeration account for 70% of total electric energy consumption. Electronics, computers and other loads are classified as MELs, which include plug loads as well as all hard-wired loads that are not responsible for space heating, cooling, water heating, or lighting. Note that MELs usually do not include major appliances such as stoves, washers, and dryers. Although each device may draw a small amount of power, MEL account for 30% of total electric energy consumption. The percentage distribution of electric energy consumption of major loads in commercial buildings is summarized in Table I. The annual electric energy consumption of typical MELs in commercial buildings in the U.S. is given in Table II. The major electrical energy consumption comes from losses of distribution transformers, water distribution and water treatment. Other electrical energy consumption comes from computers, medical equipment and vertical transport.

TABLE I

ELECTRIC ENERGY CONSUMPTION IN COMMERCIAL BUILDINGS [7]. Loads in Commercial Electric Energy Buildings Usage Lighting 31.53% Space Heating 4.73% Space Cooling 15.99% Water Heating 3.60% Ventilation 8.56% Cooking 0.90% Refrigeration 5.18% Electronics 9.46% Computers 4.73% Others 15.32% TABLE II ENERGY CONSUMPTION OF TYPICAL MELS IN COMMERCIAL BUILDINGS [8]. Typical MELs in Commercial Annual Energy Buildings Consumption (TWh) Distribution Transformers 54.5 Water Distribution 40.0 Water Treatment 24.5 Water Purification 1.1 Vertical Transport 5.1 Large Medical Equipment 5.5 Nonroad Electric Vehicles 4.0 Coffee Makers 2.7

B.

Electric Loads in Residential Sector Electricity consumption in residential sectors accounts for another 35 percent of the total electricity consumption in the U.S., and it is expected to increase 15% from 2010 to 2030 [6]. Lighting, space heating and cooling, water heating and major appliances account for 70% of total electric energy consumption; while MELs account for about 30% of total electric use in residential buildings, as shown in Table III.

TABLE III ELECTRICITY CONSUMPTION IN RESIDENTIAL BUILDINGS [7]. Loads in Residential Electric Energy Loads in Residential Electric Energy Buildings Usage Buildings Usage Lighting 6.07% Cooking 2.90% Space Heating 25.67% Furnace Fans 0.98% Space Cooling 12.59% Home Audio Eq. 0.53% Water Heating 10.67% Microwave Ovens 0.70% Refrigeration 4.84% Set-top Box 1.69% Freezers 1.31% Computer Monitors 0.39% Dishwashers 1.30% Color Television 4.48% Clothes Dryers 3.95% PCs / Laptops 1.40% Clothes Washers 0.34% All Other End Uses 19.56%

The annual electric energy consumption of typical MELs in residential buildings is given in Table IV. The major electrical energy consumption comes from Televisions, set top boxes and home entertainment devices. Most MELs in residential buildings are connected to the power all the time. Although the power consumption in standby mode is much less than the consumption in active mode power, it is estimated that about 13% of MEL usage comes from devices in standby mode. TV and other electronics manufactures have been working on reducing the standby mode power consumption. The Environmental Protection Agency (EPA) published the Energy Star 3.0 standard on November 2008

that mandates TV power consumption of less than 1 watt in standby to qualify. TABLE IV ENERGY CONSUMPTION OF TYPICAL MELS IN RESIDENTIAL BUILDINGS [8]. Typical MELs in Residential Annual Energy Buildings Consumption (TWh) Televisions 52 Set-top Boxes 17 Ceiling Fans 17 VCRs and DVDs 16 Microwaves 14 Audio 12 Rechargeable Electronics 10 Portable Electric Spas 8 Coffee Machines 4 Security Systems 2

As new technologies and materials are applied, the space heating and cooling, water heating and ventilations loads can be drastically lowered by using better insulation and natural ventilation in buildings. Light loads can also be lowered as high-efficiency light bulbs are available and cheaper on the market. However, the electric consumption percentage of MELs is likely to increase in the future, as electronics become more sophisticated and their use becomes more widespread in the buildings. III. GENERAL FRAMEWORK FOR LOAD IDENTIFICATION The electrical loads often present unique characteristics in the electric signals (i.e., voltage, current, and power). Such load characteristics provide a viable means to identify the type of a load (e.g., PC, heater, lamp, etc.) and its operational status (e.g., active, ready, standby, etc.) by analyzing the electric signals. Methods to detect electrical loads through voltage, current and power measurements have been proposed by various researchers. A general structure of the load identification system is proposed in [9-11], as shown in Fig. 1. The voltage and current waveforms of loads are continuously sampled. There are two ways for current measurements. The first way is to measure the current of each single load separately, which results in relatively high cost with the installation of sensors. Another way is to measure the current at a central point or meter point. The measured current comes from the aggregation of multiple loads, which will impair the identification accuracy. This review mainly focuses on the methods for single load identification, since they are the basics for composite load identification. After the waveforms are sampled, an event detection module is applied to decide whether a load is connected or disconnected to the system. If a load is connected, the sampled waveforms will be analyzed by a load identification module, which matches the electrical characteristic of the load to the predefined load feature database. The identified load information is sent to the load management model for a system level optimization. The challenge is to find certain electrical characteristic that are unique for a particular load and then to match a measured feature of an unknown load against a library of known features. An embedded real-time

method cannot identify many MELs in commercial and residential buildings which consume relatively same real and reactive power and become over crowded in the P-Q plane. 500

400

Reactive Power (VAR)

load identification and management system is demonstrated in [12]. For the composite load identification, the event detection module can also help to identify individual loads. A multiscale transient event detection algorithm is introduced in [1314]. It can be used to identify observed transient waveforms even when multiple transients overlap by examining the full detail of their transient behavior. A transient event detection method using voltage distortion is proposed in [15]. Another event detection and classification method for the residential buildings is proposed in [16]. It uses machine learning techniques to recognize events, such as turning on or off a light switch, a TV set, or an electric stove. An implementation of a transient event detector using a multiprocessor is explained in [17]. However, load identification only based on the event detection generally do not have good sensitivities for small electric loads since only the switching transient information is used.

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800 1000 1200 Real Power (W)

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Fig. 2. Real and reactive power of electric loads in residential buildings in the P-Q plane.

Fig. 1.

General framework of the load identification system.

IV. A REVIEW OF EXISTING METHODS Over the past twenty years, many approaches have been proposed to monitor and identify electric loads. These methods can be broadly divided into two main categories: transient state based and steady state based. Transient states rely on parameters during a transition of states of electrical devices. Methods based on steady state features are derived using constant or periodical signals when the electrical loads operate under a stable state. The following section discusses these methods. A.

Methods Based on Real and Reactive Power It has been proposed to use the variations in real and reactive power to identify the types of loads [18-23]. The real power (P) and reactive power (Q) are recorded by a measurement device. Then, the values are compared with a predefined database of load power. The real and reactive power of typical loads of residential buildings in the P-Q plane are plotted in Fig. 2 [20]. Based on the relative positions of different loads in the complex P-Q plane, the type of the load is identified. Loads that are far from each other in the plot can be identified only using real and reactive power. The success rate for load identification of large residential loads can be greater than 80% [23]. However, this

In order to increase the success rate of load identification, it is proposed in [24] to disaggregate loads based on real and reactive power using rule-based methods. The algorithm uses measurements of electric energy consumption data, along with assumptions about the customer behavior. In cases where two or more loads have similar demand levels, the algorithm uses decision analysis techniques to distinguish between them, based on the assumptions regarding to the usage of these appliances, such as the time of day or the length of usage. Another limitation to identify the load using real and reactive power is that it is based on steady-state power consumption. Therefore, it requires waiting until the transient behavior settles down so that steady-state values can be measured. However, some loads in commercial and residential buildings do not yield reliable steady-state measurements [25]. B. Methods Based on Current Waveform Characteristic and Harmonics The current waveform and the phase difference between the current and voltage include a very complete set of information to describe the load. However, it is not feasible to compare the waveform directly in application because of computation limitation and sensitivity issues. It is a challenge to extract features from the waveform for identification. Peak current, average current and RMS current have been selected for identification [26]. The current features of electric loads are first recorded into the database. The nearest neighborhood method is applied for the recognition process. However, the success rate is generally low with the limited number of features. For the loads to be identified, they have the same voltage input. Therefore, instantaneous power and instantaneous admittance have the same information as the instantaneous

current, which can be used for load identification. They are defined as • Instantaneous power:

Pinst (t ) = I (t ) × V (t )

(1)

• Instantaneous admittance: Yinst (t ) =

I (t ) V (t )

Another key steady-state signature is the power factor. It can tell apart purely resistive loads, motor-drive loads, and power electronic loads. Other related signatures include the displacement power factor and the total harmonic distortion in current. Many MELs are power electronics connected loads, which are highly nonlinear and result in current harmonics. Therefore, current harmonics are often suggested to identify these loads in [10, 18, 25, 27-30]. Current waveforms are easy to be captured and their harmonics can also be easily calculated. In order to improve the identification accuracy, they are often combined together with P-Q plane methods and transient power methods for load identification [29, 31]. A monitoring system in [25] proposes a Short-Time Fourier Transform (STFT) of current waveforms collected at sampling frequency of 8,000 Hz or higher to compute spectral envelopes that summarize time-varying harmonic content. The STFT computes estimates of the real power, reactive power, and higher frequency components of the current. Frequency domain spectrum combination with real and reactive power is used to identify these nonlinear loads. C.

(a) Space Heater (b) LCD TV Fig. 3. V-I trajectories of a space heater and a LCD TV.

(2)

Methods Based on V-I Trajectory A two-dimensional form of load signature created by the instantaneous voltage-current (V-I) trajectory, is suggested for characterizing the loads [20]. The V-I trajectory has useful engineering meanings. One study indicates that the front-end power electronic circuits and the mechanical characteristics of some electromechanical loads play critical roles in both load signature and taxonomy of electrical loads [40]. The V-I trajectories of a space heater and a LCD TV are plotted in Fig. 3. The heater is roughly a constant resistor, therefore the trajectory is linear. While the LCD TV has an internal power supply, the current is discontinuous when voltage is low. To identify different loads, the shape features of the trajectory, such as asymmetry, looping direction, area, curvature of mean line, self-intersection, and slope of middle segment, area of segments and peak of middle segment are used for the pattern recognition. A hierarchical clustering method is employed to classify the appliances and construct the taxonomy of the appliances [32]. However, this method is computationally intensive.

Remark: Above methods are based on steady-state analysis. Due to the nature of transient signals, techniques such as FFT and power factors are not defined and thus cannot be applied to transient signature analysis. Several transient state based methods are review below. D.

Methods Based on Eigenvalue Analysis and SVD Current eigenvalues are proposed for load identification in [33]. It is generally used for dynamic loads. In order to capture the load dynamics, the time series of the current waveform are first rearranged into a matrix form. The eigenvalue of this matrix is calculated by Singular Value Decomposition (SVD). The differences in eigenvalues are able to distinguish between loads with different waveform shapes. Larger loads usually have a higher first eigenvalue. This method can also be used to correlate with the power consumed by the load. However, this method is not as popular as others. The eigenvalues are only defined for square matrix. If one row has n sampled data points of one cycle, there should be n columns, i.e., n cycle of data. Otherwise, SVD need be applied instead. If the waveform is sampled at a different frequency, the matrix needs to be reconstructed and different values of eigenvalues and singular values are derived for the same load. This method loses consistence with the change of sampling rate. Another drawback is that the SVD method may have problem with overlapped or similar signatures. E.

Methods based on Transient Power Transient power feature can also be used for variable loads. It is typically calculated every half cycle. The transient behavior of a load is intimately related to the physical task that the load performs. Therefore, most loads observed in the field have repeatable transient profiles, which provide the possibility for identification of variable loads Transient harmonics power can provide extra information for variable loads, besides the transient power. It is very useful to identify variable drive connected loads, since the drive startup is generally repeatable, controlled by a microprocessor [25]. In order to analyze the transient power, continuous monitoring and high sampling rate are required. Reference [11] proposes the envelope spectral analysis method to consider fast variable “envelops”. F. Methods base on Wavelet Transform of Current Waveform Fourier transform is suitable to steady state signal analysis where the frequency components are constant with time. For

transient signals, wavelet transform can provide both frequency domain information and the corresponding locations in time simultaneously. Wavelet transforms of current waveforms is used to identify different types of power electronics connected loads [34-35]. Each type of load can be represented by multiple levels of wavelet decomposition, and the normalized energy vector of each type of load at each level is created. The energy on different levels of wavelet decomposition is used to characterize a particular type of load. G.

Comparison of Methods for Linear and Nonlinear Loads The identification accuracy of the methods above varies for different type of loads. Electrical loads in commercial and residential buildings can be classified into linear loads and non-linear loads, based on their electrical characteristic. Linear loads are loads with constant internal impedance, such as heaters. Nonlinear loads are typical loads with electronic converters (such as computers), loads with variable internal impedance (such as fluorescent lights), or loads dependent on certain process (such as Microwave Oven). The abilities of Method A to Method F to identify the typical liner and nonlinear loads are summarized and compared in Table V. TABLE V COMPARISON OF IDENTIFICATION ACCURACY FOR DIFFERENT LOADS IN BUILDINGS Methods Loads in Buildings Linear Nonlinear Loads Heaters LCD TVs, Fluorescent Washers … Chargers … Lights … … Real and Reactive Power Current Harmonics Power Factor V-I Trajectory － － Current Eigenvalues Transient Power Wavelet Transform

H.

Identification Methods Based on Optimization Loads in commercial and residential buildings are various in their electrical characteristic, the identification accuracy of the methods above vary for different types of loads. It is suggested that multiple methods should be integrated together. This problem is well suited to be solved as an optimization problem. The objective function is defined as the minimum residue while comparing the unknown loads with a set of candidates extracted from the known database, as given below, N

min Obj j = ∑ wk ( yˆ( k , j ) − y( k ) )2

(3)

k =1

where yˆ ( k , j ) is the feature k extracted from the known feature-database of load j, y( k ) is the feature k extracted from the unknown load, wk is the weight of feature k, N is the number of total features. If the load features are extracted from a single load, a oneto-one comparison with the known database can be performed. The residue between the unknown and the known

individual load signatures are. The known load with the minimum residue can then be considered as the targeted. The weight of feature, w can help to adjust the significance of features. The optimization method becomes complex when the unknown is a composite load, which contains more than one load’s signature. This problem is formulated in [36]. Integer programming is proposed for residential buildings [27]. Other methods, such as genetic algorithms [37], dynamic programming approaches [38], particle swarm optimization [39] , fuzzy logic [40] or multi-algorithm framework [41] are proposed to solve this problem. This category of methods has a major drawback, it assumes that all features of loads are already known and it is largely based on this database. However, many loads have several operating states and they consume different level of power in different states. For example, a TV set could be in operating, standby, and off modes. It generally consumes less power in standby mode than the operating mode, but the features are almost the same except in magnitudes. Thus, methods based on optimization method could be totally wrong in some cases. I.

Identification Methods Based on Intelligent Learning Except for optimization methods, intelligent learning methods, such as artificial neural networks (ANN), can be used to identify the appliance loads by teaching the ANN to learn specific features of different appliances. Through the training process, the structure and parameters of the ANN are built to capture different features of loads [42-45]. It is suggested in [43] to train a number of neural networks in cascade, which are then used as pattern classifiers to identify the various loads. Each network classifies the family in a specific level. Steady-state appliance signatures, such as fundamental frequency quantities, current, power and impedance contours and harmonic frequency current information and distortion power are considered as the inputs of neural networks. Different types of ANNs, such as multi-layer-perceptron (MLP), radial-basis-function (RBF), and support vector machines (SVM) are applied in [35, 46]. Data gathered from Fourier analysis of the input current waveform of multiple devices are used to train the models. A comparison of performance showed that multi-layer-perceptron and SVMbased models are good to determine the presence of particular loads based on their harmonic signatures. However, these methods suffer from the fact that they cannot visualize the high dimensional data before and after the pattern recognition process. Instead, SVM can give the success rate, but it cannot show the reason of the failure and which load is most likely to be recognized. Note that almost all proposed learning algorithms so far are all supervised learning. That is, data are assumed to attach tags which indicate whether the learning results are correct or not. Thus, it can be also considered as targeted learning. Other new pattern recognition methods should be taken in account for load identification.

V.

CONCLUSION

This paper has presented an up-to-date review of various methods developed for electric load monitoring and identification. No single method can identify all types of the loads in buildings, and the success rate of identification decreases dramatically as the load feature database is increased. To prioritize the loads and potentially improve the accuracy of identification, this paper has discussed the energy consumption of various electric loads in commercial and residential buildings. Based on the summary and comparison of the existing methods, it is suggested that the performance of load identification algorithm could be improved by combining the advantages of steady-state and transient timedomain methods, frequency-domain harmonic analysis methods, and event-based time-domain trending analysis methods, seamlessly integrated through optimization methods and intelligent learning techniques.

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