PreprintPDF Available
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

New devices in smart grid such as smart meters and sensors have emerged to become a massive and complex network, where a large volume of data is flowing to the smart grid systems. Those data can be real-time, fast-moving, and originated from a vast variety of terminal devices. However, the big smart grid data also bring various data quality problems, which may cause the delayed, inaccurate analysis of results, even fatal errors in the smart grid system. This paper, therefore, identifies a comprehensive taxonomy of typical data quality problems in the smart grid. Based on the adaptation of established data quality research and frameworks, this paper proposes a new data quality management framework that classifies the typical data quality problems into related data quality dimensions, contexts, as well as countermeasures. Based on this framework, this paper not only provides a systematic overview of data quality in the smart grid domain, but also offers practical guidance to improve data quality in smart grids such as which data quality dimensions are critical and which data quality problems can be addressed in which context.
Content may be subject to copyright.
Data Quality Management Framework for Smart
Grid Systems
Mouzhi Ge, Stanislav Chren, Bruno Rossi, and Tomas Pitner
Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
{mouzhi.ge,chren,brossi,tomp}@muni.cz,
Pre-print of the paper Ge, M., Chren, S., Rossi, B., Pitner, T. (2019). Data Quality
Management Framework for Smart Grid Systems, to appear in the 22nd International
Conference on Business Information Systems (BIS2019).
Abstract. New devices in smart grid such as smart meters and sensors
have emerged to become a massive and complex network, where a large
volume of data is flowing to the smart grid systems. Those data can be
real-time, fast-moving, and originated from a vast variety of terminal
devices. However, the big smart grid data also bring various data quality
problems, which may cause the delayed, inaccurate analysis of results,
even fatal errors in the smart grid system. This paper, therefore, iden-
tifies a comprehensive taxonomy of typical data quality problems in the
smart grid. Based on the adaptation of established data quality research
and frameworks, this paper proposes a new data quality management
framework that classifies the typical data quality problems into related
data quality dimensions, contexts, as well as countermeasures. Based on
this framework, this paper not only provides a systematic overview of
data quality in the smart grid domain, but also offers practical guidance
to improve data quality in smart grids such as which data quality di-
mensions are critical and which data quality problems can be addressed
in which context.
Key words: smart grid, data quality, data quality problem, smart meter
1 Introduction
Smart grids are developed to optimize the generation, consumption, and man-
agement of energy via intelligent information and communication technology. Its
research involves smart meters, user-end smart appliances, renewable energy re-
sources, digitalization in electricity supply networks, as well as new technologies
to detect and react to the changes in electricity supply networks. As [1] stated,
a smart grid reflects a combination between Information and Communication
Technologies (ICT) and Internet of Things (IoT), whereby data services such
as aggregation of sensor data and analysis of voltage consumption from smart
meters [28] offer a foundation for the concept of smartness. Since the quality of
data can directly affect the output of data services, the security, quality, reliabil-
ity, and availability of an electric power supply depends on the quality of data
in the power system [19]. Thus, data quality has been considered as a prominent
2 Mouzhi Ge, Stanislav Chren, Bruno Rossi, and Tomas Pitner
issue in smart grids [8]. In a broader context, data quality has become a crit-
ical concern to the success of organizations [15]. Numerous business initiatives
have been delayed or even canceled, citing poor-quality data as the main reason
[16]. Therefore, data quality management can be regarded as an indispensable
component in smart grid applications.
The current data quality problems in smart grid are addressed still in an
ad-hoc style. For example, Chen et al. [8] focused on the outlier detection of
electricity consumption data. Their solution tackles a specific quality aspect of
electricity consumption data. However, this will obstruct practitioners to fore-
see the other data quality problems and delay the reaction on time for potential
data quality problems. Thus, it is valuable to obtain a big picture of the different
data quality problems in the smart grid network. Also, some of the data quality
problems in smart grids may be interconnected. One data quality problem may
be caused by another data quality problem. For example, an outlier in the elec-
tricity consumption data may be caused by missing data items or data attacks.
Therefore, focusing on specific quality aspects can mislead the root causes of
the data quality problems. Based on our review, there is a lack of a systematic
framework for managing data quality in smart grids. Also, data quality is critical
in the smart grid domain, as invoices of end users depend for example on the
collected power consumption data.
In this paper, we propose a systematic data quality management framework
for smart grids. It can not only profile a variety of data quality problems in
the smart grid context, but also show how to categorize and organize the data
quality problems based on data quality dimensions. In this framework, different
data quality problems are identified and assigned to the related dimensions. It
can therefore indicate which data quality dimensions are critical in the smart
grid data quality improvement. Furthermore, the data quality problems assigned
in the same dimension may need to considered together.
The remainder of the paper is organized as follows. Section 2 reviews the
general data quality management and the state-of-the-art data quality research
in Smart Grid. Section 3 identifies and summarizes a comprehensive set of the
possible data quality problems in smart grid. Based on the identified data quality
problems, Section 4 proposes a framework to categorize the data quality prob-
lems into established data quality dimensions. Finally, section 5 concludes the
paper and outlines the future research.
2 Data Quality and Smart Grid Research
Since the data quality problems are usually domain-specific, the importance of
data quality dimensions may vary in different application domains. For example,
Ge et al. [17] conducted a study to rank the overall importance of different
data quality dimensions used in a variety of data quality studies. They further
emphasized that prioritizing the importance of data quality can determine the
focus of data quality improvement and management. Therefore, to find out which
dimensions are important in the smart grid domain, assigning the data quality
Data Quality in Smart Grid 3
problems to dimensions can be used to facilitate the data quality measurement
process.
There exists some research work that tends to classify the data quality prob-
lems in smart grids. for example, Chen et al. [8] proposed that the data quality
issues in electricity consumption data can be divided into noise data, incomplete
data and outlier data. Noise data refer to the data with logical errors or the data
violating certain rules or specifications. These data that can in turn affect data
analysis results. Incomplete data mean the missing values in the data sources,
and outlier data are the data that deviate from standard data variation ranges.
However, the scope of data in smart grids is broader than electricity consumption
data. For example, [24] specified various types in smart grids such as sensor data,
battery status data, and device downtime data. Therefore, the classification of
the data quality problems in smart grids can be extended to a larger scale.
The smart grid domain encounters the Big Data Quality problems. Due to
the massive number of smart meters, various sensors and other customer facil-
ities, the smart grid network has been generating Big Data [11]. Zhang et al.
[35] further described the big data characteristics in smart grids such as large
amount of meter and sensor data (volume), real-time data exchange (velocity),
and extensive data sources in a smart grid (variety). They further stated that
data quality is a critical issue in processing the big smart grid data, where the
data quality management is usually positioned in the data preprocessing phase.
Zhang et al. [35] classified the big data quality problems by using three counter-
measures, which are data integration, data cleansing, and data transformation.
While data integration deals with the entity resolutions and data redundancy,
data cleansing can be used to alleviate missing and abnormal data. Finally, data
transformation serves to provide high-quality data formats for data analytics
such as correcting data distribution and constructing new data attributes. This
classification is especially designed for smart grid data analytics. In this paper,
we will outline big and normal data quality problems in smart grids.
3 Data Quality in Smart Grid
When we discuss the term ”data” in the context of smart grids, we cannot ignore
the overall complexity of the infrastructure and the communication needs [10].
Due to the complexity, data in smart grids comes from a variety of sources, and
can be structured, unstructured, but very often a mixture of both, making the
analysis more complex [35].
3.1 Smart Grid Infrastructure
The smart grid infrastructure comprises several parts, each of them with dif-
ferent responsibilities regarding the energy and data transfer. The smart grid
architecture depends on the standards used. According to the NIST standard,
the smart grid has a hierarchical structure that includes the following domains:
4 Mouzhi Ge, Stanislav Chren, Bruno Rossi, and Tomas Pitner
Wide Area Network (WAN) is responsible for communication between power
generation plants, substations and transformer equipment. Neighbourhood Area
Network (NAN) serves as a bridge between customer premises and the sub-
stations. This level focuses on the collection of data from the smart meters,
which are aggregated by the data concentrator and further transferred to the
data centers [10]. Furthermore, the customer premises network (CPN) consists
of networks at the customer location. Depending on the type of customer, we
can distinguish between Home Area Networks (HAN), Industrial Area Network
(IAN) and Business Area Network (BAN). This layer enables communication
between the smart meters, intelligent appliances and their connectivity to NAN.
An overview of communication technologies in smart grids is shown in Fig. 1.
Fig. 1. Communication infrastructure in smart grids (from Al-Omar et al. [2])
3.2 Data in Smart Grid
Generally, smart grid data can be classified in three categories: measurement
data (e.g., smart meters data), business data (e.g., customer data), and external
data (e.g., weather data) [35]. In this context, we focus our analysis on measure-
ment data, that is the type of data that can, more than other types, characterize
SG data analysis needs. We focus in particular on data derived from two devices:
Smart Meters [10] and PMUs [25, 5].
In the smart grid infrastructure, there are two main components which pro-
duce the measurement data essential for the grid operation: smart meters and
phasor measurement units (PMUs). Smart meters are devices which serve as
replacements of traditional power meters installed at customer premises (e.g.
households, industrial buildings, etc...). They record data about customer’s
power consumption (and possibly production if the customer utilizes renew-
able power sources). Smart meters enable two-way communication and a power
Data Quality in Smart Grid 5
distributor is also able use them to remotely control appliances such as water
heaters. This becomes useful in various load management programs to balance
the power flow in the grid. Besides the measurement data, smart meters are also
able to report various events, for example meter failures, unexpected manipu-
lation with the device or occurrence of over/under-voltage states [31]. On the
other hand, PMUs are devices which measure phasor information in the power
distribution, such as voltage and current. The PMUs collect the measurement
data from many points in the power grid at very high frequency (up to 120 sam-
ples per second). The data are time synchronized based on the GPS radio clock.
Measurement data are transmitted to various monitoring systems using them to
analyze the current state of the power grid to discover potential stability issues.
There is a number of systems in smart grids that ensure reliability of the
power supply and the availability of critical services and which rely on high
quality data collected from smart meters or PMUs [9]: (1) Blackout Prevention
Systems protect the grid from instabilities and failures. They cover the whole
power grid, using the data from PMUs to obtain relevant information from the
grid. (2) Supervisory Control and Data Acquisition Systems (SCADA) are one of
the core systems of a Smart Grid that provide monitoring and support to oper-
ation activities and functions in transmission automation, dispatch centers and
control rooms. In a SCADA system, a remote terminal unit collects data from
smart meters or devices in a substation and delivers the data to a central Energy
Management System. (3) Flexible Alternating Current Transmission Systems are
responsible for reliable and secure transmission of power. They allow dynamic
voltage control, increased transmission capability and capacity, and support fast
restore of the grid after failure. (4) Feeder Automation Systems are responsible
for the operation of medium-voltage networks including fault detection.
3.3 Data Quality Problems
Issues in data collected by smart grid devices are usually referred by literature
as either bad data [29], corrupted [6], or missing data [6]. However, such defini-
tions do not capture the diversified facets of smart grid data issues that are more
refined in terms of specific issues. For example, Shishido and Solutions [32] dis-
cuss issues in smart meter data quality during the consumption data collection
process. The main issues reported are duplicate items from the meter readings,
zero record periods, and large spikes over periods of time. There are some is-
sues in Smart Meters/PMU data that are peculiar of the smart grid context:
non-trustful data points, data aggregation issues due to privacy concerns, tim-
ing issues with skewed timestamps of recorded events. We summarize the main
Data Quality Problems in Table 1, as a series of issues that are derived from
literature on smart meters and PMU data analysis.
Duplicate records from multiple devices (DQP1) mean that the same record
is stored multiple times in the same way or with different values, causing dupli-
cation in the data [32]. A suggested strategy for the identification is cross-linking
records across different devices to look for possible duplicated values, as well to
search for repeating sequences [32].
6 Mouzhi Ge, Stanislav Chren, Bruno Rossi, and Tomas Pitner
Table 1. Data Quality Problems in the Smart Grid context.
DQ Problem Description Context Countermeasures
DQP1. Duplicate
data
Duplicate records from Smart Me-
ter reading, can be caused by
upgrading of Smart Meters (e.g.,
same reading from the old and new
SM) [32].
SM Cross-linking data from
multiple devices and
examining repeating
sequences [32]
DQP2. Missing/
incomplete data
Some data can be expected to be
available (e.g. regular smart meter
reading) but due to some reasons
(e.g. technical failure) they are not
[32].
PMU/SM Linear interpolation
(short periods), creation
of daily load profiles for
historical patterns recre-
ation (longer periods)
[22, 29]
DQP3. Zero
Records Seman-
tics
Detecting differences between data
that was not transmitted/recorded
by sensors and stand-by periods.
All lead to difficulties in interpret-
ing zero-valued ranges [32, 29].
SM Creation of daily load pro-
files [22, 29], reasonability
tests for allowed ranges
and comparison of values
from other devices [34]
DQP4. Data
Outliers (out-of-
range)
Large bursts (spikes), or low val-
ues compared to the average over
a period of time [32, 20]
PMU/SM Reasonability tests for al-
lowed ranges [34], appli-
cation of anomaly detec-
tion algorithms, context-,
collective-based [31, 20].
DQP5. Measure-
ment Errors
Datapoints that represent mea-
surement errors due to hardware
failures, signal interference, etc...
[29]
PMU/SM Reasonability tests for al-
lowed ranges and compar-
ison of values from other
devices [34], signal anal-
ysis of smart meters for
outliers detection [30]
DQP6. Non-
trustful data-
points
Datapoints that were manipulated
intentionally (e.g. data injection
attacks: alter the measurements of
SMs to manipulate the operations
of the smart grid [23, 7, 27])
PMU/SM Using historical data,
statistical-based detec-
tion [23]
DQP7. Data
anonimization
Aggregation of attributes/features
for privacy preservation / anoni-
mization can lead to issues for data
analysis [12]
SM Preserving data integrity
for smart grid data
aggregation, e.g. by hash-
ing/signature checking
against data tampering
[26], Smart Metering data
de-pseudonymization [21]
DQP8. Timing
issues
Timing in which an event is
recorded by PMUs / Smart Me-
ters is not precise, causing difficul-
ties in the integration of data, or
in case of PMUs, wrong computa-
tions [34, 13]
PMU/SM Comparing values
recorded by different
systems, e.g. PMU and
SCADA [34]
Data Quality in Smart Grid 7
Missing/incomplete data (DQP2) represents the case in which data record-
ings are missing for some periods of time, making this a problem of data impu-
tation research [22]. Strategies in such cases go in the direction of linear interpo-
lation for short periods of missing records, or the creation of daily load profiles
for historical patterns recreation in case of longer periods [22, 29].
Zero record periods (DQP3) constitute a distinct case from the aforemen-
tioned missing/incomplete data scenario [32]. In this case, data is present but
with zero recordings, making difficult to understand if such records were not
recorded/transmitted, or missing values were due to some stand-by period [29].
There are different strategies that can be applied to understand the semantic of
zero-record periods of time, like the creation of daily load profiles from smart
meter data [22, 29], or reasonability tests for allowed ranges and comparison of
values from other devices for PMUs [34].
Data outliers or out-of-range values (DQP4) represent large bursts of data
spikes or low values compared to the average values over periods of time [32].
Detection of these value ranges is part of the anomaly detection area, determining
outliers based on context-, or collective-based algorithms [20, 31]. For PMUs,
reasonability tests for allowed ranges are important [34]
Measurement errors (DQP5) can represent a relevant issue for both smart
meters and PMUs [29]. There can be many sources of such issues in smart
grids data. According to Chen et al. [6], measurement errors can derive from
smart meter problems, communication failures, equipment outages, lost data,
interruption/shut-down in electricity use, but also components degradation and
operational issues [3]. To address measurement errors, reasonability tests for
allowed ranges and comparison of values from other devices can be used in the
context of PMUs [34], while signal analysis of smart meters for outliers detection
can be applied to smart meter data [30].
Non-trustful data points (DQP6) derive from potential cyber-attacks to the
smart grid infrastructure. Such attacks do not only involve authentication issues,
but also false data injection attacks to provide fake data-points as they were
real recorded events [23, 7]. The non-trustful data-points injected/modified by
means of cyber-physical attacks, are meant to manipulate the overall operations
in the SG by leading operators into false beliefs about the current state of the
infrastructure [27].
Data aggregation issues due to privacy concerns (DQP7) come from the needs
to preserve the privacy of data collected from smart meter readings. Some fea-
tures collected by the different types of devices might be obscured or aggregated
into other features, making the analysis process more difficult. Over the last
years, many techniques have been developed to preserve the statistical proper-
ties of aggregated features [12], preserve data integrity from tampering [26], and
algorithms that attempt at data de-pseudonymization [21]. While the removal of
some features might be seen as a way to anonimize data from smart meters, this
is however ineffective, as customers can be re-identified by other features [4].
Timing issues with skewed timestamps of recorded events (DQP8) can be an
issue in both smart meters and PMUs. While in smart meters such errors might
8 Mouzhi Ge, Stanislav Chren, Bruno Rossi, and Tomas Pitner
just involve issues in later data attribution between different devices [13], for
PMUs such issues can involve subsequent wrong computations [34]. Comparing
data from different devices can be a strategy to detect and correct timing issues,
such as comparing timestamps from PMUs and SCADA systems [34].
Furthermore, there are two main peculiarities in smart grid data cleansing
activities: (1) data are mostly generated from sensors, hardware-devices: root
causes can be found in hardware failures, communication related problems [14];
(2) data cleansing in the data mining domain usually assume structural data,
while in the smart grid context, mostly time-series approaches are needed for
the identification of patterns/anomalies.
4 Data Quality Management Framework in Smart Grid
To propose a data quality management framework for smart grids, we have
adopted the general data quality framework from Wang and Strong [33]. Thus,
intrinsic, contextual, representational and accessibility are adopted to catego-
rize the data quality concept. Further, the relations between data quality cat-
egories and data quality dimensions are also adopted from Wang and Strong
[33]. However, since not all the data quality dimensions are important for smart
grids, we have used the identified data quality problems to select the data qual-
ity dimensions in our framework. Therefore, our framework is intended to be
domain-specific for smart grids. Based on the typical data quality problems that
we revisited in the smart grid context, we derive the data quality framework in
smart grid that classifies these data quality problems into dimensions, categories,
as well as into contexts. This data quality framework is divided into five layers.
The first layer from the top is the overall data quality in smart grid. This layer
is usually used as one step in the whole big data analytics process e.g. before or
after the data integration. The second layer divides the smart grid data quality
into four quality aspects. Under each data quality aspect, it is the dimension
layer that indicates which data quality dimensions are related to which smart
grid data quality problems. Therefore, the third layer and fourth layer are data
quality dimensions and specific problems. As data quality problems are derived
from different contexts, the context in smart grids is the final layer.
It can be seen that there are seven data quality dimensions that are particu-
larly important for the smart grid domain. These seven data quality dimensions
are accuracy, consistency, timeliness, completeness, believability, accessibility,
and interpretability. Accuracy is mainly defined as the data points falling into
a normal range or interval. Thus, data outliers in smart grids belong to this
dimension and detected data outliers can be considered as inaccuracy. The con-
sistency dimension is used when there are different sources of smart grid data.
In the smart grid domain, data can be generated from different devices. On one
hand, this creates the data redundancy, on the other hand, the cross-reference
approach can be used to validate the data consistency. Since time series analysis
is usually used in smart grids, timing issues like wrong timestamps may cause
problems to construct the time series data. Therefore, the timeliness dimension
Data Quality in Smart Grid 9
Fig. 2. Data quality framework in smart grid
is to control if the data are recorded in a precise time or time interval. Com-
pleteness dimension involves the problems of missing and incomplete smart grid
data. As there is a large number of devices in smart grids, the data completeness
issue can be regularly caused by device malfunction. Although the believability
dimension is not well discussed in other domains, trustful data are important in
smart grids because the data manipulation in smart grid can be directly related
to economical benefits. The accessibility dimension is related to the hardware
and infrastructures in smart grid. Therefore, accessibility can be used to mea-
sure if the data can be accessed. Finally, interpretability is defined as how well
the data can be interpreted. This can be balanced between the data privacy and
the analytic details. Overall, not all the data quality dimensions from previous
research are critical for the smart grids domain.
Our framework is proposed in an operational and measurable level. For ex-
ample, each data quality dimension can be measured by its related data qual-
ity problems. Likewise, the data quality categories such intrinsic or contextual
data quality can be further measured by aggregating the related dimensions.
Since most of the general data quality management frameworks are not domain-
specific, their model granularity is refined only to the dimension level: it is there-
fore difficult to apply other data quality management frameworks and measure
data quality in a domain. Our framework tackles this problem and relates the
data quality measurement to specific quality problems.
Our framework can be further integrated into other data quality manage-
ment frameworks. Since most of the existing data quality models or frameworks
are based on the data quality dimensions [18], our framework is centralized by
10 Mouzhi Ge, Stanislav Chren, Bruno Rossi, and Tomas Pitner
data quality dimensions and can be easily integrated into other frameworks or
models by replacing the dimensions from this framework. Furthermore, our pro-
posed framework can locate the root cause of low data quality dimensions by
concrete quality problems in smart grids. After the assessment, the contexts and
countermeasures can then be used for data quality improvement. For example,
to determine which data quality problems occurred in which context and to
determine the countermeasures to improve some data quality dimensions.
5 Conclusions
In this paper, we have proposed a systematic and practical taxonomy of data
quality problems in smart grids. We have then proposed a new data quality
management framework that adapted the data quality aspects and refined them
to seven critical data quality dimensions for smart grids. Thus, the data quality
assessment and improvement in smart grids can be more focused on the derived
dimensions. Each data quality dimension is connected to concrete smart grid
data quality problems. On one hand, the framework enables the data quality
measurement for data quality dimensions. On the other hand, since the data
quality problems are linked to specific smart grid contexts, it can facilitate to
identify the root causes of low quality data and establish a data quality improve-
ment plan. Compared to other general data quality frameworks, our framework
is designed to be domain-specific and limited to the smart grid. The framework
contributes towards automatically controlling the data quality in smart grid. As
future work, we plan to further extend the framework by automating the data
quality measurement processes.
6 Acknowledgements
The research was supported from ERDF/ESF ”CyberSecurity, CyberCrime and
Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0-
/16 019/0000822).
References
[1] Abo, R., Even, A.: Managing the quality of smart grid data research in
progress. In: IEEE Int. Conference on Emerging Technologies and Innova-
tive Business Practices for the Transformation of Societies. pp. 5–8 (2016)
[2] Al-Omar, B., Al-Ali, A., Ahmed, R., Landolsi, T.: Role of information and
communication technologies in the smart grid. Journal of Emerging Trends
in Computing and Information Sciences 3(5), 707–716 (2012)
[3] Alahakoon, D., Yu, X.: Smart electricity meter data intelligence for future
energy systems: A survey. IEEE Transactions on Industrial Informatics
12(1), 425–436 (2016)
Data Quality in Smart Grid 11
[4] Buchmann, E., B¨ohm, K., Burghardt, T., Kessler, S.: Re-identification of
smart meter data. Personal and ubiquitous computing 17(4), 653–662 (2013)
[5] Burnett, R.O., Butts, M.M., Sterlina, P.S.: Power system applications for
phasor measurement units. IEEE Computer Applications in Power 7(1),
8–13 (1994)
[6] Chen, J., Li, W., Lau, A., Cao, J., Wang, K.: Automated load curve data
cleansing in power systems. IEEE Transactions on Smart Grid 1(2), 213–221
(2010)
[7] Chen, P.Y., Cheng, S.M., Chen, K.C.: Smart attacks in smart grid commu-
nication networks. IEEE Communications Magazine 50(8) (2012)
[8] Chen, W., Zhou, K., Yang, S., Wu, C.: Data quality of electricity consump-
tion data in a smart grid environment. Renewable and Sustainable Energy
Reviews 75, 98 – 105 (2017)
[9] Chren, S., Rossi, B., Buhnova, B., Pitner, T.: Reliability data for smart
grids: Where the real data can be found. In: 2018 Smart City Symposium
Prague. pp. 1–6 (2018)
[10] Chren, S., Rossi, B., Pitner, T.: Smart grids deployments within eu projects:
The role of smart meters. In: Smart Cities Symposium Prague, 2016. pp.
1–5. IEEE (2016)
[11] Daki, H., El Hannani, A., Aqqal, A., Haidine, A., Dahbi, A.: Big data man-
agement in smart grid: concepts, requirements and implementation. Journal
of Big Data 4(1), 13 (2017)
[12] Efthymiou, C., Kalogridis, G.: Smart grid privacy via anonymization of
smart metering data. In: Smart Grid Communications, First IEEE Inter-
national Conference on. pp. 238–243. IEEE (2010)
[13] Eichinger, F., Pathmaperuma, D., Vogt, H., M¨uller, E.: Data analysis chal-
lenges in the future energy domain. Computational Intelligent Data Anal-
ysis for Sustainable Development pp. 181–242 (2013)
[14] Gao, J., Xiao, Y., Liu, J., Liang, W., Chen, C.P.: A survey of communi-
cation/networking in smart grids. Future Generation Computer Systems
28(2), 391–404 (2012)
[15] Ge, M., Helfert, M.: A framework to assess decision quality using informa-
tion quality dimensions. In: Proceedings of the 11th International Confer-
ence on Information Quality, MIT, USA. pp. 455–466 (2006)
[16] Ge, M., Helfert, M.: Effects of information quality on inventory manage-
ment. International Journal of Information Quality 2(2), 177–191 (2008)
[17] Ge, M., Helfert, M., Jannach, D.: Information quality assessment: validating
measurement dimensions and processes. In: 19th European Conference on
Information Systems, Helsinki, Finland. p. 75 (2011)
[18] Ge, M., O’Brien, T., Helfert, M.: Predicting data quality success - the bull-
whip effect in data quality. In: Perspectives in Business Informatics Research
- 16th International Conference, Copenhagen, Denmark. pp. 157–165 (2017)
[19] Gellings, C.W., Samotyj, M., Howe, B.: The future’s smart delivery system.
IEEE Power and Energy Magazine 2(5), 40–48 (2004)
12 Mouzhi Ge, Stanislav Chren, Bruno Rossi, and Tomas Pitner
[20] Jakkula, V., Cook, D.: Outlier detection in smart environment structured
power datasets. In: Sixth International Conference on Intelligent Environ-
ments. pp. 29–33. IEEE (2010)
[21] Jawurek, M., Johns, M., Rieck, K.: Smart metering de-pseudonymization.
In: 27th Annual Computer Security Applications Conf. pp. 227–236 (2011)
[22] Kim, M., Park, S., Lee, J., Joo, Y., Choi, J.K.: Learning-based adaptive
imputation methodwith knn algorithm for missing power data. Energies
10(10), 1668 (2017)
[23] Kosut, O., Jia, L., Thomas, R.J., Tong, L.: Malicious data attacks on the
smart grid. IEEE Transactions on Smart Grid 2(4), 645–658 (2011)
[24] Leonardi A, Ziekow H, S.M.K.P.: Dealing with data quality in smart home
environments lessons learned from a smart grid pilot. Journal of Sensor and
Actuator Networks 5(1) (2016)
[25] Li, F., Qiao, W., Sun, H., Wan, H., Wang, J., Xia, Y., Xu, Z., Zhang,
P.: Smart transmission grid: Vision and framework. IEEE transactions on
Smart Grid 1(2), 168–177 (2010)
[26] Li, F., Luo, B.: Preserving data integrity for smart grid data aggregation.
In: Smart grid communications, 2012 IEEE third international conference
on. pp. 366–371. IEEE (2012)
[27] Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state
estimation in electric power grids. ACM Transactions on Information and
System Security 14(1), 13 (2011)
[28] Matta, N., Rahim-Amoud, R., Merghem-Boulahia, L., Jrad, A.: Putting
sensor data to the service of the smart grid: From the substation to the
ami. Journal of Network and Systems Management 26(1), 108–126 (2018)
[29] Peppanen, J., Zhang, X., Grijalva, S., Reno, M.J.: Handling bad or missing
smart meter data through advanced data imputation. In: IEEE Innovative
Smart Grid Technologies Conference. pp. 1–5. IEEE (2016)
[30] Rao, R., Akella, S., Guley, G.: Power line carrier (plc) signal analysis of
smart meters for outlier detection. In: Smart Grid Communications, IEEE
International Conference on. pp. 291–296. IEEE (2011)
[31] Rossi, B., Chren, S., Buhnova, B., Pitner, T.: Anomaly detection in smart
grid data: An experience report. In: Systems, Man, and Cybernetics, IEEE
International Conference on. pp. 2313–2318. IEEE (2016)
[32] Shishido, J., Solutions, E.U.: Smart meter data quality insights. In: ACEEE
Summer Study on Energy Efficiency in Buildings (2012)
[33] Wang, R.Y., Strong, D.M.: Beyond accuracy: What data quality means to
data consumers. Journal of Management Info. Sys. 12(4), 5–33 (1996)
[34] Zhang, Q., Luo, X., Bertagnolli, D., Maslennikov, S., Nubile, B.: Pmu data
validation at iso new england. In: Power and Energy Society General Meet-
ing (PES), 2013 IEEE. pp. 1–5. IEEE (2013)
[35] Zhang, Y., Huang, T., Bompard, E.F.: Big data analytics in smart grids: a
review. Energy Informatics 1(1), 8 (2018)
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Abstract Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.
Article
Full-text available
This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.
Article
Full-text available
Over the last years, we have witnessed increasing interconnection between the physical and digital world. The so called Internet of Things (IoT) is becoming more and more a reality in application domains like manufacturing, mobile computing, transportation, and many others. However, despite promising huge potential, the application domain of smart homes is still at its infancy and lags behind other fields of IoT. A deeper understanding of this type of techno-human system is required to make this vision a reality. In this paper, we report findings from a three year pilot that sheds light on the challenges of leveraging IoT technology in the home environment. In particular, we provide details on data quality issues in real-world deployments. That is, we analyze application level data for errors in measurements as well as issues in the end-to-end communication. Understanding what data errors to expect is crucial for understanding the smart building domain and paramount for building successful applications. With our work, we provide insights in a domain of IoT that has tremendous growth potential and help researchers as well as practitioners to better account for the data characteristics of smart homes.
Article
Full-text available
A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.
Article
Full-text available
One of the requirements of a smart grid (SG) is making the electrical network and its subsystems aware of their condition. The deployment of various sensing devices plays an essential part in achieving this goal. Nevertheless, data generated by this deployment needs to be well managed so that it can be leveraged for operational improvement. Data aggregation is perceived as an important technique for managing data in the SG in general, and in its Advanced Metering Infrastructure (AMI) in particular. Indeed, data aggregation techniques have been used in order to reduce communication overhead in SG networks. However, in order to fully take advantage of the aggregation process, some level of intelligence should be introduced at concentrator nodes to make the network more responsive to local conditions. Moreover, by using a more meaningful aggregation technique, entities can be accurately informed of any disturbance. This paper contributes an agent-based approach for data and energy management in an SG. It also proposes CoDA, a correlation-based data aggregation technique designed for the AMI. CoDA employs fuzzy logic to evaluate the correlation between several messages received from Smart Meters (SMs). Analysis and simulation results show the benefits of the proposed approach w.r.t. both packet concatenation and no aggregation approaches.
Conference Paper
Full-text available
In recent years, we have been witnessing profound transformation of energy distribution systems fueled by Information and Communication Technologies (ICT), towards the so called Smart Grid. However, while the Smart Grid design strategies have been studied by academia, only anecdotal guidance is provided to the industry with respect to increasing the level of grid intelligence. In this paper, we report on a successful project in assisting the industry in this way, via conducting a large anomaly-detection study on the data of one of the power distribution companies in the Czech Republic. In the study, we move away from the concept of single events identified as anomaly to the concept of collective anomaly, that is itemsets of events that may be anomalous based on their patterns of appearance. This can assist the operators of the distribution system in the transformation of their grid to a smarter grid. By analyzing Smart Meters data streams, we used frequent itemset mining and categorical clustering with clustering silhouette thresholding to detect anomalous behaviour. As the main result, we provided to stakeholders both a visual representation of the candidate anomalies and the identification of the top-10 anomalies for a subset of Smart Meters.
Conference Paper
Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run.
Conference Paper
The Smart Grid (SG) concept reflects the integration of Information and Communication Technologies (ICT) together with Internet of Things (IoT) technologies into the electrical power grid, which enables devices to produce data regarding their energy consumption and, by that, to manage the electricity consumption and production in a smarter way. The vast amounts of data generated and processed in SG environments raise the issue of data quality (DQ) management. The “Quality of Context” approach observes DQ from a business-value perspective, focusing more on data contents and use and less on its physical characteristics. Accordingly, this study explores two SG-relevant DQ aspects - sampling frequency and density. The former reflect the high sampling rate needed for ubiquitous computing environments such as the SG, while the latter reflects real-world limitations on sensor infrastructures. As the study progresses, our goal is to further conceptualize these DQ dimensions and evaluate their impact in both simulated and real-world SG environments, toward defining mechanism for their optimal configuration.
Article
With the increasing penetration of traditional and emerging information technologies in the electric power industry, together with the rapid development of electricity market reform, the electric power industry has accumulated a large amount of data. Data quality issues have become increasingly prominent, which affect the accuracy and effectiveness of electricity data mining and energy big data analytics. It is also closely related to the safety and reliability of the power system operation and management based on data-driven decision support. In this paper, we study the data quality of electricity consumption data in a smart grid environment. First, we analyze the significance of data quality. Also, the definition and classification of data quality issues are explained. Then we analyze the data quality of electricity consumption data and introduce the characteristics of electricity consumption data in a smart grid environment. The data quality issues of electricity consumption data are divided into three types, namely noise data, incomplete data and outlier data. We make a detailed discussion on these three types of data quality issues. In view of that outlier data is one of the most prominent issues in electricity consumption data, so we mainly focus on the outlier detection of electricity consumption data. This paper introduces the causes of electricity consumption outlier data and illustrates the significance of the electricity consumption outlier data from the negative and positive aspects respectively. Finally, the focus of this paper is to provide a review on the detection methods of electricity consumption outlier data. The methods are mainly divided into two categories, namely the data mining-based and the state estimation-based methods.