NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Extracting Operating Modes 

from Building Electrical Load 

Data

Preprint

Stephen Frank 

Colorado School of Mines

Luigi Gentile Polese, Emily Rader, 

Michael Sheppy, and Jeff Smith 

National Renewable Energy Laboratory

Presented at the 2011 IEEE Green Tech Conference

Baton Rouge, Louisiana

April 14 – 15, 2011

Conference Paper

NREL/CP-5500-49636 

January 2012
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1 

Extracting Operating Modes from

Building Electrical Load Data

Stephen Frank 

Division of Engineering

Colorado School of Mines

Golden, Colorado, United States of America

stfrank@mymail.mines.edu

Luigi Gentile Polese, Emily Rader,

Michael Sheppy, and Jeff Smith

Electricity, Resources, & Building Systems Integration

National Renewable Energy Laboratory

Golden, Colorado, United States of America

luigi.gentile.polese@nrel.gov

Abstract—Empirical techniques for characterizing electrical 

energy use now play a key role in reducing electricity 

consumption, particularly miscellaneous electrical loads, in 

buildings. Identifying device operating modes (mode extraction) 

creates a better understanding of both device and system 

behaviors. Using clustering to extract operating modes from 

electrical load data can provide valuable insights into device 

behavior and identify opportunities for energy savings. We 

present a fast and effective heuristic clustering method to identify 

and extract operating modes in electrical load data.

Keywords–buildings, clustering, miscellaneous electrical loads,

mode extraction, system characterization.

I. INTRODUCTION

Miscellaneous Electrical Loads (MELs) are the fastest 

growing load category in buildings and account for the largest 

portion of commercial and residential electricity consumption.

MELs are defined as all building electrical loads apart from

heating, ventilation, and air-conditioning (HVAC), space 

lighting, and water heating [1]. The U.S. Department of Energy 

(DOE) estimates that MELs will grow from 31% of total 

commercial building primary energy use in 2006 (1610 TWh or 

5.5 Quads) to 43% in 2030 (3140 TWh or 10.7 Quads); 

residential buildings have similar statistics. Since commercial 

and residential buildings consume 40% of U.S. primary energy

(29300 TWH or 100 Quads in 2006), MELs account for around 

10% of all energy used in the United States [2], [3]. 

MELs represent a growing opportunity for energy savings. 

Characterizing MEL behavior is a key step toward energy 

reduction. Mode extraction—the identification and 

quantification of operating states (modes)—is an important 

technique for load characterization. Data collection and mode 

extraction for MELs are difficult to implement, however,

because many end use subcategories fall under the umbrella of 

“miscellaneous” load. Several studies have specifically 

addressed MELs, particularly plug loads [1], [4]-[7], but few

sources of load data are available for specific MELs.

Targeted metering of individual MELs can alleviate the

lack of available load data. As part of a larger DOE effort, the

National Renewable Energy Laboratory (NREL) is conducting 

a study of commercial MELs. The study targets a large retail 

store with a wide variety of space types, including general 

merchandise, grocery, restaurant, pharmaceutical, and medical 

services. NREL has catalogued more than 750 distinct MEL 

devices and metered approximately 240 of these, recording

power, voltage, and energy use. Characterizing and modeling

MEL behavior are primary outcomes of the study, both to 

identify energy saving opportunities and to improve energy 

simulations. A rapid, reliable method of identifying and 

extracting MELs power consumption characteristics is needed 

to create energy models. Here, we present a fast and effective 

heuristic clustering technique for extracting operating modes 

from electrical load data. 

II. RELATED WORK

Several studies have explored MEL operation, including 

extraction of operating modes. A recent study of MELs in U.S. 

commercial buildings [1] used building-level electrical data to 

estimate national MELs energy use by device and building 

type. This study is among the most rigorous in combining 

independent MELs data sets, but its results have significant 

uncertainty because many analysis inputs are assumptions, 

averages, or best estimates. A large-scale residential study in 

Europe [4] employed an integrated approach consisting of 

monitoring studies, household surveys, and detailed audits. The 

study focused on characterizing and reducing standby power 

consumption as defined in the IEC62301 standard [8], 

however, as opposed to overall power consumption. 

More limited studies have targeted specific MEL categories 

via field monitoring. One study examined a subset of office 

MELs via intensive monitoring over a two week period [5]. A 

similar study that focused on residential uses in Germany [6]

analyzed and characterized power modes and operating times 

of information and communication devices according to four 

pre-defined operating modes: normal, standby, off-mode, and 

off. Another study employed non-intrusive methodology 

consisting of an inventory and a night-time audit of the power 

status of office equipment and devices [7]; it recorded data on 

the types, power states and power save delay settings of office 

equipment in a commercial building environment. This study 

consisted only of a snapshot at a single point in time, however,

and therefore yielded no information about the time spent in 

each power state.
2 

Non-intrusive load monitoring (NILM) offers a different 

approach to load characterization, for both MELs and 

traditional loads. NILM uses a single, centralized instrument to

monitor aggregated electricity consumption. Computer

software then disaggregates individual loads from the overall

signal, recording device behavior in the process. The simplest

and least expensive NILM techniques measure changes in real

and reactive power levels and require only low frequency

sampling [9]-[11]. More complex techniques rely on harmonic

analysis to distinguish loads; these require costlier hardware

and more sophisticated software [12], [13]. NILM can reliably

distinguish on/off loads, but loads with multiple or variable

states present a much greater challenge [9], [14], [15]. NILM is

less disruptive and less costly to deploy than a plug-level 

metering and can track mobile loads precisely. However,

NILM must be preceded by analysis of the individual loads to 

be monitored because NILM algorithms can disaggregate 

individual loads only by comparison with known load 

behavior. Therefore, NILM is not a suitable first step in load

characterization.

III. MODE EXTRACTION VIA CLUSTERING

Cluster analysis, or clustering, is the process of dividing a 

data set into logical groupings (clusters) based on similarity 

criteria. Mode extraction for electrical loads may be viewed as 

a clustering problem: each power mode represents a distinct 

cluster within the load data that must be identified and 

extracted. The resulting modes (clusters) may then be further 

analyzed to determine characteristics such as duty cycle, 

average mode power, and expected mode transitions. 

For example, the vending machine power data of Figure 1 

suggest four operating modes: a dominant mode near 700 W 

and three distinct but closely spaced modes in the range of 150-

250 W. Clustering the data provides a method for detecting and 

quantifying these power modes.

A. Data Set Challenges

Electrical load data present several challenges that make

effective clustering difficult. Long-term time series of electrical

loads may contain several hundred thousand data points, 

recording steady-state device operation, transient conditions,

and noise. Many assumptions that give tractability to other

clustering problems do not hold for electrical load data: the

number of clusters is not known a priori, they may not conform

to Gaussian distributions, and they may be of different

densities. Electrical loads, particularly MELs, differ widely 

with respect to key characteristics, including magnitude, 

characteristic load profile, and cycling frequency. This load 

diversity creates difficulty during clustering algorithm 

initialization, as little information is available in advance. For

loads encountered in the field, even basic nameplate data are

often unavailable or unclear.

Instrumentation imposes further challenges because a cost

versus data quality tradeoff is inherent in widely deployed 

sensor networks. Data transmission and storage capabilities

limit time resolution and the number of parameters recorded. 

Measured data are discretized to the precision of the

instrument. Poor-quality instrumentation can also create excess 

noise, which must be discarded during mode extraction.

B. Algorithm Requirements

In characterization studies involving many loads, clustering

must be performed on a large number of data sets representing

diverse devices. The algorithm employed should operate with

minimal user input, as manual initialization is tedious when

many data sets must be processed. The algorithm should also 

be robust with respect to the challenges identified in section IIIA. An ideal clustering algorithm requires no initialization, is

fast, is computationally efficient for large data sets, identifies 

and removes noise in the data, identifies clusters of arbitrary

shapes, and accurately determines cluster boundaries.

No presently available clustering algorithm fulfills all these

requirements. In practice, the user must at minimum set global 

defaults for the algorithm and specify which measurements to

use. The case where default parameters may be automatically 

computed from data set characteristics represents a suitable 

level of automation. Also, clustering becomes more tractable

when it is performed using only a few dimensions. Here, we 

limit cluster analysis to device power only. 

C. Clustering Algorithms

Numerous cluster analysis techniques are available, many

of which are summarized in [16] and [17]. Only a few common 

partitioning techniques are discussed here, as many clustering 

techniques are too computationally expensive for application to 

large data sets [18]. Centroid-based techniques, such as Kmeans, are a subclass of partitioning techniques that classify

the data into k clusters based on the distance to k central points, 

where k is an input variable. Such techniques must be iterated 

for differing values of k when the number of clusters is not

known. This greatly increases algorithm run time. 

Another class of partitioning methods applies the

expectation-maximization (EM) algorithm for statistical

modeling to finite mixtures, for instance, a set of Gaussian 

Figure 1. Example of time series load data recorded from a vending machine 

used as an input for mode extraction via clustering.
3 

distributions [19]. EM-based models must select both the 

number of clusters and an appropriate statistical model a priori. 

If this information is not known, the technique may perform

very poorly. EM-based algorithms are more computationally

expensive than centroid-based techniques, as they require

complex statistical calculations.

Density estimation partitioning techniques define clusters

as regions of high density within the data. In contrast to the 

previous techniques, density estimation requires no a priori

information about the number or shape of data clusters. The

DBSCAN algorithm and its variants use a nearest-neighbor 

definition of density to perform clustering [18], [20], [21]. 

Except in cases where Fast Nearest Neighbor algorithms are

available, the computational cost of these techniques scales 

with the square of the data set size [18]. 

IV. THE HISTOGRAM HEURISTIC APPROACH

Because density estimation requires little input prior to

clustering, it readily lends itself to automation; however, 

density-based approaches are computationally expensive. We 

developed a rapid approach called the histogram heuristic (HH) 

technique, which uses a histogram to estimate data density. The 

technique is based on the concept of visual classification of 

electrical load data and automates an otherwise tedious manual 

process. It is fast, scales linearly, and yields robust results

across a wide range of inputs. It is, however, strictly heuristic;

there is no guarantee of optimality according to formal criteria.

A. The Algorithm

The core of the HH technique is the construction of a 

histogram to represent the data. Given sufficient data points

and appropriate bin selection, a histogram can approximate the

probability density function of a data set. The algorithm may 

then examine the histogram rather than the underlying data. 

This greatly reduces the computational requirements. 

The main steps in the HH technique are:

1. Construct a histogram of the data. 

2. Identify the largest histogram bin. 

3. Identify surrounding histogram bins that exceed a userspecified threshold. 

4. Classify the data in these bins as a new cluster. 

5. Remove the bins from the histogram.

6. If enough points have been classified into clusters, 

stop. Otherwise, return to step 2.

Steps 2 and 3 represent cluster identification; steps 4 and 5 

represent cluster extraction. Figure 2 illustrates steps 2-5 in one 

dimension. The full version of the algorithm includes 

additional error checking to ensure that true clusters are not 

split apart because the threshold value was poorly selected. 

B. Parameter Selection

The HH technique requires specification of the number of 

histogram bins, the bin height threshold during clustering, and 

the minimum amount of data to classify. The number of 

histogram bins drives the resolution at which cluster boundaries 

may be detected because the width of each bin is inversely 

proportional to the number of bins used. Generally, more 

histogram bins are desirable because they enable the algorithm 

to identify tightly spaced clusters. There are two practical 

limitations on the number of histogram bins:

• The width of each bin may not be smaller than the 

precision of the data. Otherwise, some bins will fall 

entirely between allowable values of the data (and will 

be empty), artificially dividing the histogram. 

• If the number of bins is increased to the point that 

each bin contains very few points, the histogram loses 

smoothness and no longer accurately approximates the 

probability density function. 

In practical cases with large data sets, several hundred bins 

may be used without approaching either limitation. 

Figure 2. Simplified representation of the histogram heuristic clustering 

technique in one dimension.
4 

The bin height threshold controls how the algorithm 

identifies cluster edges: all adjacent bins above this threshold 

are included in the cluster. The algorithm uses a relative 

threshold in order to compensate for the number of bins and 

adapt to varying cluster densities. If the bin height threshold is 

too high, the algorithm will truncate the cluster edges. If it is 

too low, the algorithm will attach bordering noise points to the 

cluster and may merge multiple clusters together.

The data classification stopping point controls the algorithm 

by specifying a minimum percentage of the data that must be 

classified into clusters. This number should be set based on the 

expected noise level. For example, if the data set contains 1% 

noise, the data classification threshold should be set at 99% or

lower. In general, setting this threshold too low will not affect 

cluster discovery unless one or more valid clusters contain only 

a very small number of data points. Setting this threshold too 

high has an immediate and obvious effect: the algorithm 

identifies many additional small clusters of noise points.

C. Advantages and Disadvantages

The HH technique’s primary advantages are simplicity and 

speed. The algorithm requires no assumptions about the data 

structure, but still forms intuitive clusters. The bulk of the 

computation occurs during construction of the histogram, so 

the computational cost of the HH technique scales linearly. 

Finally, the HH technique offers reliable noise rejection. 

The HH technique has some limitations: 

• It requires sufficient data to create a smooth 

histogram. (This limitation does not usually apply to 

electrical load data sets, which are typically large.) 

• It is sensitive to the initialization of control 

parameters; however, it is not difficult to select default 

parameters that function well across a wide range of 

input data sets. 

• It rarely identifies cluster edges perfectly. 

• It is unsuited to data sets with adjacent or overlapping 

clusters because the density-based clustering process 

cannot distinguish between them. 

• It does not extend readily to data sets of high 

dimensionality. Increasing the dimensionality by one 

order decreases the number of data points per bin 

(reducing histogram smoothness), squares the number 

of bins required to produce sharp cluster edges, and 

doubles the number of search directions (increasing 

computational cost). Therefore, in practice the HH 

technique is limited to clustering in only a few 

dimensions.

V. PERFORMANCE EVALUATION

To evaluate the HH technique’s performance with respect 

to electrical load data, we used the R environment for statistical 

computing [22] to implement and test it. During controlled

testing, we compared the HH techniques with three other

clustering methods: K-means, EM-based normal mixture 

modeling (MClust) [23], and DBSCAN [24]. We also applied 

the HH technique to 163 sets of recorded MELs data to gauge 

performance with real data. 

A. Performance Testing

During controlled testing, we performed three distinct tests: 

algorithm sensitivity, quality, and speed. The results of the 

sensitivity test determined the default algorithm parameters 

used in the remaining tests.

1) Sensitivity: This test measured the sensitivity of the HH 

algorithm results to changes in the control parameters. 

Preliminary testing showed that the data classification 

stopping point has little effect on the clustering results as long 

as it is set below the level of the noise. In the test we varied 

the number of bins and the bin height threshold while keeping 

the data classification threshold constant at 99%. The test 

showed that the HH algorithm is robust across a wide range of 

input parameters. The number of bins may vary from 

approximately 30 to 3000 and the data classification threshold 

from approximately 0.1% to 10% without significantly 

altering the clustering results. Furthermore, the domain of 

input parameters corresponding to consistent results varied 

little among differing MELs data sets. Default values exist for 

these input parameters that perform well with diverse data: we 

selected 250 histogram bins and a threshold level of 1.0% for 

the speed and quality tests.

2) Speed: The speed test evaluated execution time of each 

algorithm using an identical set of control data. The HH 

algorithm is the fastest of those tested due to its simplicity (see 

Figure 3). HH algorithm execution time scales linearly with 

data set size.

Figure 3. Comparison of histogram heuristic execution time with three other 

clustering algorithms.
5 

3) Quality: The quality test compared the quality of output 

among the four clustering algorithms. Figure 4 displays a 

qualitative comparison of the four algorithms as applied to the 

vending machine data of Figure 1. K-means and MClust 

artificially split the uppermost mode and combine two of the 

lower modes; only the HH and DBSCAN algorithms return 

groupings that closely match the inuitive visual power modes. 

However, HH fails to correctly capture the lower bound of the 

highest power mode and DBSCAN artificially introduces 

several small clusters at that bound.

In order to quantify clustering accuracy numerically, we 

constructed a set of control data with three distinct modes 

(clusters) plus 1% white noise. (The data was constructed to 

resemble the observed behavior of a beverage refrigerator.) 

We initialized each algorithm according to recommended 

practice, including specifying the number of clusters for the Kmeans algorithm. We then calculated accuracy by matching 

indentified and true clusters via best matching of cluster 

centers and computing the percentage of correctly clustered 

points. Table I summarizes the results. The HH and DBSCAN 

algorithms both achieved excellent accuracy. However, 

DBSCAN identified several small, spurious clusters that the 

HH algorithm did not (similar to its behavior in Figure 4).

B. Application: Miscellaneous Electrical Load Data 

We applied the HH algorithm to 163 MELs data sets, 

plotting each with color-coded modes for rapid visual 

inspection (not shown in this paper). We inspected the plots, 

qualitatively assessing the accuracy of the algorithm compared 

with intuitive groupings. Of the data sets analyzed, 26 were 

single mode devices and 137 were multimodal. The algorithm 

properly identified all single-mode devices. In 75% of the 

multimodal cases, the clusters returned were sufficiently 

similar to manually identified clusters to be considered 

accurate. The most common misidentifications (61%) were a 

failure of the algorithm to capture mode edges accurately (as in 

Figure 4) or the identification of too many modes. An 

TABLE I. CLUSTERING ACCURACY WITH RESPECT TO CONTROL DATA

Algorithm Num. Clusters Found Accuracy

Histogram Heuristic 3 + Noise 99.9%

K-means 3 88.7%

MClust 6a 84.9%

DBSCAN 9 + Noise 99.8%

a. MClust classifies noise as an independent cluster

Figure 4. Comparison of clustering results for vending machine load data returned from four clustering algorithms:

Histogram Heuristic, K-means, MClust, and DBSCAN.
6 

additional 14% of the errors represent the lumping of two 

visually distinguishable modes into one because of close or

overlapping clusters.

The remaining errors were mostly the result of loads with 

spiking power use (for example, microwave ovens) for which 

increased time resolution is needed or mode extraction may not 

be an appropriate method of characterizing device behavior. In 

general, the HH technique rapidly and effectively identified the 

same modes produced by visual inspection. Although a

significant number of misidentifications can be corrected by 

altering the input parameters on a case-by-case basis, doing so 

is time consuming and demonstrates the need to further refine 

the default input parameters. In cases where the algorithm 

performed particularly poorly, visual inspection of the data

often revealed either that little or no logical clustering was 

possible or the presence of some type of data corruption. 

In future work, the algorithm could be improved by 

implementing automatic detection of corrupt data. Other areas 

for improvement include developing techniques to set the input 

parameters based on the characteristics of the input data, 

allowing user-guided searches, and implementing correction by 

cross-correlation to other data parameters, such as voltage or 

ambient temperature.

VI. CONCLUSIONS

The HH technique can effectively identify and extract 

operating modes from electrical load data, performing 

significantly faster than other, more general clustering 

algorithms or manual analysis. This allows rapid, automated 

analysis of electrical load data in preparation for load 

modeling. The HH technique provides a foundation on which 

to build a fully automated electrical mode detection system. 

The technique provides a path for large-scale MELs 

characterization, a critical step in decreasing the amount of 

energy used by MELs in buildings.

ACKNOWLEDGMENT

The authors thank C. Lobato of NREL for his assistance 

with data collection throughout the project. The authors also 

thank Dr. L. Brackney of NREL for his technical assistance 

and feedback, including extensively reviewing this paper. 

Finally, the authors thank Dr. P.K. Sen of Colorado School of 

Mines for his review of this paper. 

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