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Informatic
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Hopf Energy Informatics (2018) 1:4
https://doi.org/10.1186/s42162-018-0009-3
RESEARCH Open Access
Mining volunteered geographic
information for predictive energy data
analytics
Konstantin Hopf
Correspondence:
konstantin.hopf@uni-bamberg.de
Information Systems and Energy
Efficient Systems Group, University
of Bamberg, Kapuzinerstraße 16,
96047 Bamberg, DE, Germany
Abstract
Background: Users create serious amounts of Volunteered Geographic Information
(VGI) in Online platforms like OpenStreetMap or in real estate portals. Harvesting such
data with the help of business analytics and machine learning methods yield promising
opportunities for firms to create additional business value through mining their internal
and external data sources. Energy retailers can benefit from these achievements in
particular, because they need to establish richer customer relations, but their customer
insights are currently limited. Extending this knowledge, these established companies
can develop customer-specific offerings and promote them effectively.
Methods: This paper gives an overview to VGI data sources and presents first results
from a comprehensive review of these crowd-sourced data pools. Besides that, the
value of two exemplary VGI data sources (OpenStreetMap and real estate portals) for
predictive analytics in energy retail is investigated by using them in a household
classification algorithm that recognizes specific household characteristics (e.g., living
alone, having large dwellings or electric heating).
Results: The empirical study with data from 3,905 household electricity customers
located in Switzerland shows that VGI data can support the recognition of the 13
considered household classes significantly, and that such details can be retrieved
based on VGI data alone.
Conclusion: The results demonstrate that the classification of customers in relevant
classes is possible based on data that is present to the companies and that VGI data can
help to improve the quality of predictive algorithms in the energy sector.
Keywords: Volunteered geographic information, Energy data analytics, Energy retail,
Predictive analytics, Machine learning, Household classification
Background
Predictive data analytics becomes increasingly important for organizations and enable
them to remain competitive and agile in continuously changing markets (Constantiou
and Kallinikos 2015; Gillon et al. 2012; Mithas et al. 2013;Sharmaetal.2014). This holds
especially for energy utilities that are exposed to a groundbreaking market transition,
affecting them from the production and sales perspective. On the energy production side,
the current fossil-nuclear energy supply is currently being replaced by renewable energy
sources (Dangerman and Schellnhuber 2013), which forces utility companies to high
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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Hopf Energy Informatics (2018) 1:4 Page 2 of 21
investments into their fixed assets. On the energy retail side, their market becomes more
competitive due to market liberalizations in many countries and the fact that private
spendings for energy are not much increasing, since the share of wallet for housing,
water and energy remains nearly constant during the last 20 years in Europe at 20–25%
(Eurostat 2017). Business model innovations in the utility industry are therefore crucial
(Markard and Truffer 2006).
Big data analytics in the energy industry
Utility companies hold, however, millions of data points on their customers that can be
developed to a valuable business asset. This available data will further increase in the
future due to the roll-out of smart meter and Internet of things infrastructures. Tech-
niques of big data analytics, especially predictive information systems will help utility
companies to make sense of that data (Sharma et al. 2014;Zhouetal.2016) and can enable
them to create innovative products and services. Even though, first steps in development
of algorithms and tools for predictive energy data analytics are done and basic knowledge
on how to create insights from big data exists, but the currently existing models are often
not reliable enough to be used in practice. Therefore, further research on better predictor
variables, more suitable data processing and knowledge modeling techniques is neces-
sary. This work contributes to this research gap and investigates the use of freely available
geographic data for predictive energy data analytics in the specific but relevant example of
household classification.
Predictive analytics in energy retail: the use of household classification
A possible reaction to the harsher market requirements for energy retailer is to improve
the customer communication and to offer product or services that better fit to the needs
of their customers (Gebauer et al. 2014), establish targeted customer communication
or one-to-one marketing. Examples for such products are tariffs for specific customer
groups, energy consulting, and energy- or infrastructure-related cross-sales (e.g., heating
and cooling appliances, photovoltaic installations, Internet access). For the development
and commercialization of such products and services, detailed knowledge about cus-
tomers is necessary. Typical variables needed in marketing campaigns are household
characteristics (e.g., families with children, household type and size) or knowledge on
the customers’ attitudes and intentions (e.g., towards sustainability, purchase intentions
for products). Recent studies have shown, that various household characteristics (such as
household type, type of the heating system, number of residents, etc.) can be identified
using 30-min electricity smart meter data (Beckel et al. 2014). Based on 15-min smart
meter and hourly weather data, household characteristics related to energy-efficiency can
be identified (Sodenkamp et al. 2017;Hopfetal.2018). Other studies show that house-
holds with old heating systems can be identified to be addressed in an energy-efficiency
campaign (Kozlovskiy et al. 2016), or customers using an electric vehicle (Verma et al.
2015), which is also an interesting insight for energy utilities. In all studies on household
classification, several attempts have been made to improve the quality of predictive arti-
facts. Multiple data preparation and machine learning algorithms have been tested so far,
but the models still need further improvements and lowered error rates to become more
reliable and ready for real applications in the industry. I argue that additional predictor
variables need to be taken into account to increase the quality of household classification
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Hopf Energy Informatics (2018) 1:4 Page 3 of 21
models, since the information that is possible to extract from electricity consumption data
is limited. In this paper, I therefore present VGI as a promising source of additional data
that is freely available online and has emerged in the past years.
Volunteered geographic information as a data source for predictive analytics
A significant number of web portals arose during the past years, due to the fact that
users collaborate and create crowd-sourced data that often has a geographic reference
associated. Goodchild (2007)coinedthetermVGI for this phenomenon of user-
generated geospatial data. His work attracted a considerable amount of research in the
past ten years and lead also to attention in other disciplines than geography. Unique
features of this data source are the free availability and the variety of subjects – even for
some subjects, data was never-collected before (Sester et al. 2014).
In this work, all user-generated geospatial data is considered as VGI as long as it
was collected with the (explicit or implicit) consent of the user. This definition does not
include data provided or published in a non-voluntary way (e.g., accidentally published or
revealed by hackers).
This definition is necessary here, since there is no clear consensus in related works on
the concept of VGI. On the one hand, there is the understanding of VGI in a strict sense,
restricting it to user contributions that are made “with the intent of providing informa-
tion about the geographic world” (Elwood et al. 2012). Examples for this interpretation of
VGI are the co-creation of maps (e.g., OpenStreetMap, Wikimapia) or the collection of
environmental data (e.g., Christmas Bird Count). On the other hand, user contributions
without the intrinsic motivation to publish geographic data exist, since users create for
tweets, posts, likes, reviews, photos or other content that has a spatial datum. Stefanidis
et al. (2013) name such information Ambient Geospatial Information (AGI), but they are
seen as VGI in a broader sense in this paper. Harvey (2013) suggests to delimit the term
VGI to data that was collected with a clear opt-in of users. He suggests the term Con-
tributed Geographic Information (CGI) for all geographic data that was collected without
that clear consent, but having an opt-out possibility for users as. Harvey names “cell phone
tracking and RFID-equipped transport cards” as examples, but the use of such data, out-
side the objective it was collected for, stays obviously often in conflict with privacy or
data protection regulations. Therefore, the above given definition of VGI in contrast to
non-voluntary generated data is preferred here.
A vast number of VGI initiatives are already documented in the literature: (Elwood
et al. 2012) mention 99 initiatives that they found in 2009 and categorize them according
to geographic extend, the date initiated, the sponsoring entity and the primary purpose
of the initiative. Ballatore et al. (2013), Sester et al. (2014), such as Rinner and Fast
(2015) document further platforms and suggest different categorization schemes for ini-
tiatives, by focusing on the usage of VGI data, the data types and formats used. Due the
dependence on continuous contributions from users, a large turnover of newly emerging,
changing and perishing projects is visible. Based on the existing collections (Elwood et al.
2012;Seeetal.2016), I collected 116 VGI initiatives that have been documented in the
literature and analyzed them together with students. The list was further extended with
a number of real estate advertisement portals where users can search for hiring, renting,
selling or buying houses and dwellings. In total, we identified a sample of 127 initiatives.
This collection is not intended to be an extensive list of VGI initiatives, but to give some
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Hopf Energy Informatics (2018) 1:4 Page 4 of 21
idea of the types and categories of such data sources. From the 116 VGI initiatives that
stem from the literature, only 64 (53.7%) still have an active web site or could be found via
the Internet1. However, some initiatives have become very popular and attracted many
active users (such as OSM, or Geocaching).
By looking at the distribution of active initiatives according to data types (Rinner and
Fast 2015) and categories (Sester et al. 2014)inFig.1, one can see that the majority
of projects deal with location data, mapping, or environmental and social media topics.
The category “business” is also frequent, but this figure is misleading, since ten German-
speaking real estate portals were assigned to this category that might over-represent
this category in comparison to others. Generally, VGI initiatives contain a considerable
amount of data that is related to an energy context and is therefore relevant for the util-
ity industry, given that knowledge from the freely available data sources can be derived.
However, the mining of such data sources is associated with large effort to access the data.
For example, only 26 of 75 active platforms have an explicit API for data access.
So far, the use of VGI data in the energy domain is sparse, but could bring value to
energy retailers and service providers in the utility industry. This paper aims therefore to
motivate the use of VGI, illustrates the usage of such data and the possible contribution for
predictive data analytics in the field of energy retail considering two prominent examples
of VGI initiatives.
Research Objective and Methodology
Research presented in this article aims to provide an answer the research question: To
what extend can volunteered geographic information improve the quality of predictive
models in energy data analytics, in the specific case of household classification?
The approach to answer this research question implies two steps of data analysis: In a
first step, the VGI data is be made accessible for the further analytical procedure. This
implies to use techniques from cartography, data integration, data fusion and data gen-
eralization (Sester et al. 2014), because the user-generated data is very heterogeneous in
terms of existing data structures, producers and its quality. In a second step, the usefulness
of VGI data is assessed by using the data for household classification and calculating
Fig. 1 Frequencies of n=75 active VGI initiatives 2017, according to data types (Rinner and Fast 2015)and
categories (Sester et al. 2014); some portals have multiple data types and categories associated
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Hopf Energy Informatics (2018) 1:4 Page 5 of 21
performance gains. This goes along with the suggestion of Mondzech and Sester (2011)
to assess the quality of VGI by means of application needs.
Starting with the mining of OSM as a first data source for energy data analytics, that
contains geographic information and Points of Interest (POIs) data, online portals for real
estate advertisements (containing information on energy-efficiency, rental prices, living
quality, etc.) are evaluated.
Related Work
To the best of my knowledge, the first application of VGI in energy data analytics was
mentioned in our recent paper (Hopf et al. 2016). Likewise, Zhou et al. (2016) mention
several data sources in their literature review on big energy data analytics, but do not
mention VGI data. However, the use of VGI data was documented for many applica-
tions and it was shown that this data source is valuable. Examples are the eHealth-domain
(Eysenbach 2008;Mooneyetal.2013), or disaster management (Haworth and Bruce 2015)
where volunteers brought quick help into crisis areas. A prominent example is the Haitian
Earthquake (Zook et al. 2010), where a large number of volunteers worldwide provided
remarkable aid and created very detailed maps for emergency forces within days. The tra-
ditional creation of such maps by professional map-makers would have taken much more
time (Anhorn et al. 2016;Horitaetal.2013).
In the field of energy data analytics, the identification of household characteristics based
on customer data from utility companies was investigated in several studies. The liter-
ature can be grouped into two categories based on the applied methods: unsupervised
machine learning (also known as clustering or segmentation) using energy consumption
from utility customers and other data sources (Beckel et al. 2012; Chicco 2012;Kwacetal.
2013;McLoughlin2013). Because of their nature, these analyses give descriptive insights
on groups of customers, but do only provide cluster-membership information on the level
of single households. Thus, further interpretations of experts are necessary to use the
results in practice.
Supervised machine learning methods are used to identify household properties based
on energy consumption data (this field is also known as ’household classification’). The
approach was first mentioned by Beckel et al. (2013,2014) who show that prediction
of individual household characteristics is possible using 30-min electricity smart meter
data. This approach was improved (Hopf et al. 2016) and adapted it for 15-min smart
meter data to identify energy-efficiency related household properties (Sodenkamp et al.
2017;Hopfetal.2018). Similar approaches using smart meter data have been used to
detect heat pumps (Fei et al. 2013), predicting occupancy in residential homes (Albert
and Rajagopal 2013), the enrollment in energy-efficiency campaigns (Zeifman 2014),
or the existence of electric vehicles in households (Verma et al. 2015). However, these
studies investigate only single characteristics of households, but not multiple as in the
household classification studies.
The usage of smart meter data for such analysis in often limited due to technical
(e.g., low number of smart meters installed), organizational or legal reasons (e.g., sales
departments may not have access to high-resolution data). It is therefore reasonable to
investigate to what extend household classification is feasible using annual electricity con-
sumption data together with the household location, since both is available in energy
retail for billing purposes. Recently published results of household classification studies
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Hopf Energy Informatics (2018) 1:4 Page 6 of 21
with annual electricity consumption and location data show promising results (Hopf et
al. 2016;Hopfetal.2017), but the currently achieved prediction accuracy needs further
improvements for practical applications. The investigation of further freely available data
sources, such as VGI is therefore reasonable.
Household classification based on annual electricity consumption and location
information
The household classification methodology used in this work is illustrated in Fig. 2.It
consists of two dimensionality reduction and data preparation steps (empirical feature
extraction and automatic feature selection), followed by the training and test of suit-
able supervised machine learning algorithms. For the supervised machine learning part,
multiple algorithms (e.g., k-Nearest-Neighbor, Support Vector Machines, Naïve Bayes,
Random Forest, Artificial Neural Networks) are candidates for the artifact development
and the most suitable algorithm is then chosen. Typically, Breiman’s (2001) Random For-
est algorithm yield good results for the investigated classification problems (Hopf et al.
2016;Hopfetal.2017), but also in other studies (Fernández-Delgado et al. 2014). Finally,
the classification results are evaluated using well-recognized performance metrics (Hastie
et al. 2009, chap. 7) such as Accuracy, Precision, Recall or Area Under ROC Curve (AUC).
In the remaining section, a detailed description of the empirically defined electricity and
geographic features is given as an example of the necessary data transformation in energy
(big) data analytics and the use of VGI data.
Empirical and automatic dimensionality reduction
Model building is one of the core techniques used in big data analytics (Abbasi et al. 2016),
since knowledge, insights and business value does not automatically come from simply
applying analytic tools to data. It must be leveraged by analysts and managers into strate-
gic and operational decisions to generate value (Müller et al. 2016;Sharmaetal.2014).
One of the major challenges in sense-making from big data is to identify the relevant
features and encounter the “curse of dimensionality” (Keogh and Mueen 2011) lowering
the quality of predictive systems. This problem is serious: Whereas in the last decade,
Fig. 2 Household classification and evaluation methodology
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Hopf Energy Informatics (2018) 1:4 Page 7 of 21
a number of 50-100 features was called a “large” feature set (Kudo and Sklansky 2000),
today we are confronted with hundreds or even thousands (Hua et al. 2009)offeatures.
Much research has been conducted on automatic dimensionality reduction – also
known as feature selection. The interested reader is referred to the general introduction
of Guyon and Elisseeff (2003) or the comprehensive literature reviews made by Chan-
drashekar and Sahin (2014), Liu and Motoda (2008) or Saeys et al. (2007). However, the
capability of such automatic feature selection algorithms is limited, at least for the investi-
gated class of machine learning problems in energy retail. Empirical definition of relevant
features is therefore a cornerstone in the development of such tools, since it enables the
modeling of human knowledge into the data. By defining features empirically, also expert
knowledge or commonsense can be coded into the data.
Electricity consumption features
In this study, annual electricity consumption is considered that is available to all utility
companies for billing purpose. In previous works (Hopf et al. 2016;Hopfetal.2017), three
features for such data have been suggested and used that are also applied in this study:
1. Logarithmic annual consumption, normalized by the number of days in which the
energy was consumed
2. Consumption trend as the relative change between the consumption of different
years (obtained with a linear regression model)
3. Neighborhood comparison as the Z-score of the household’s logarithmized nor-
malized consumption deviation from its neighborhood (in the postal code region)
Geographic features from OpenStreetMap data
The empirical definition of features for VGI map data is done using the currently largest
VGI initiative OSM (Jokar Arsanjani et al. 2015) as the study subject. The data in this
project is – typically for geographic map data – characterized by multiple spatial dimen-
sions and contextual data that cannot be easily used in data analytics methods. In detail,
the data in OSM consists of points, poly-lines and spatial relations that are annotated
with tags (containing plain-text data) to give semantic meaning to the objects. Therefore,
feature extraction a necessary step making the data accessible in energy data analytics.
The feature extraction is done using problem specific knowledge applied to the data
to derive meaningful variables (Guyon and Elisseeff 2006): Some features are directly
derived from the raw data (e.g., frequencies of objects in an area). Besides that, a literature
survey in geographic information science was conducted and spatial landscape metrics
(e.g., describing the spatial envelope or the structure of a landscape) were identified as
domain-specific features. The identified features were grouped into topologic features
describing the structure of and relations between one household and spatial neighbors
(e.g. longitude, latitude, frequency objects in the surroundings, distance to city center),
and semantic features considering the meaning of an object within the spatial context it
appears. In the group of semantic features, I differentiate between point-based features
and polygon-based features (i.e. building and land use). In the remaining section, the
categories of features are described.
Topologicfeatures— In geodesy, topology focuses on the structure of and relations
between spatial objects, e.g., whether two objects overlap or whether one object is
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Hopf Energy Informatics (2018) 1:4 Page 8 of 21
contained in another (Becker 2012). In contrast to the semantic relation of a geographical
object to another, the topology covers only shape and arrangement of objects. Example
topologic features are geographic location (longitude, latitude, altitude) that representing
the local environment, such as climate conditions or the number of sun hours per day.
The number of objects in the surrounding of a household is a proxy for the population
density and distance to city center is a distinction criterion for urban or rural area
Semantic features — A charted point or polygon in a map can have different meaning,
depending on the size of the object, in which region it appears and which objects are
nearby. The semantics focuses on the meaning of an object within the context it appears.
The available source for semantic geographical data are tag-annotations associated to
objects in OSM.
It is obviously difficult to express the proximity of two or more objects by means of
a semantic distance. Suggested similarity measures, as used for example in information
retrieval (Schwering 2008; Janowicz et al. 2011; Ballatore et al. 2012), can not be easily
adapted in the context of this study, since they mainly express the similarity of two objects,
but not the similarity of areas on a map as needed for the characterization of households.
Therefore, I defined semantic features from the OSM internal taxonomy for tags of geo-
graphic objects that is known as OSM map features (the double meaning of this term
cannot be avoided here) that is maintained by the OSM community.
The community defines 26 primary map features that are recommended to be used
(others may be used, but are mostly infrequent), but this tag-taxonomy is not well suited
for the use in energy data analytics:
•map features have no direct meaning for the field of application (such as
aeroway
,
barrier
or
emergency
)
•the wide range of some map features must be diversified (such as
amenity
,
natural
or
man_made
)
•map features cover an overlapping field of objects (for example
office
,
craft
,
shop
,
amenity
and
building
can describe the existence of company locations)
•some of the tags are not included in the set of primary map features, but are
interesting for the field of application, because they are frequently used and cover
important aspects of the surrounding of a house
Geographic Object Categories (GOCs) for semantic feature definition — To ov e r-
come with these ambiguities and the incompleteness of the map feature definition, a set
of GOC was defined that describes the relevant geographic information in the context
of energy data analytics. All tags that belong to the primary map features (including the
keys and the values) are associated to one or more GOCs. All GOC are then used to
define semantic features for classification. These definition of GOCs and the derivation
of features for later classification was done in three steps:
1.
Top-down:
all primary map features and their tags2have been investigated and
meaningful groups of objects have been derived.
2.
Bottom-up:
actual tag frequencies in the surrounding of ca. 4,000 households were
downloaded from OSM, analyzed and used for group definition.
3.
Feature derivation:
For each GOC, features for the classification are defined.
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Hopf Energy Informatics (2018) 1:4 Page 9 of 21
The result of this categorization is shown Table 1. Nine point-based GOCs (marked
with ) and two polygon-based categories are defined. The polygon-based categories are
buildings (marked with ) and land use (marked with ♠). Map features that belong to
object categories are marked with X. Consecutively, the calculation of features based on
the categorization is described.
Point-based features on landmarks and POIs ()— Most of GOCs describe point-
information on a map regardless of whether the objects are represented as points or
polygons in the OSM database, since there is no standard on how to map POIs. On the
one hand, some building-polygons are directly tagged as restaurants, public institution,
etc. On the other hand, POIs are mapped as separate points. The characterization of one
household, however, may rely on the frequency of POIs or the distance to it. The size
or shape of a restaurant or the next park is considered as unimportant in this context.
Therefore, nine GOCs are defined as point-based categories and the following features
are defined for each category (each calculated using a predefined map section, usually a
rectangular box of 500 ×500 m)
1.
Frequency
: the total number of objects in this GOC
2.
Average distance
: the arithmetic mean of all distances from the household location
to the objects
3.
Minimal distance
: the distance to the nearest object in the GOC to the household
location
Features on buildings ()—The category of buildings constitute a separate GOC
because they are of main concern in energy data analytics. This category is solely affected
by map feature building. Other map features (e.g., levels, building:levels, building:roof,
roof:shape) may contain additional information, but do not identify polygons as buildings.
The following semantic features are defined for all buildings:
1. Size of the buildings (mean and variance)
2. Distance to buildings (mean and variance)
3. Type of the next building
4. Type of building that contains the household location on the map
5. Most frequent building type in the surrounding
Features about land use (♠)— Finally, land use information constitutes a separate cat-
egory. Within this category, three land use types are distinguished: residential, city,and
countryside. The following features are defined for land use information:
1. The land use type the household location lies in
2. The area of the land use polygon the household location lies in
3. The total area of all three land use types
Feature extraction from real estate portals
As a second source of VGI data, publicly available online real estate advertisements
are considered. This data is user-generated and has – in most cases – a geographic
location associated. In the context of household classification, the use of this data source
seems natural, given that household and building characteristics are often similar in
neighborhoods.
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Hopf Energy Informatics (2018) 1:4 Page 10 of 21
Table 1 Overview to the definition of GOCs based on primary and secondary map features in the OSM database
OSM map feature # objects Building Land use ♠Public Institution Business Food Transportation Recreation Culture Sights Countryside Road system
Primary Map Features
Aerialway <5 X
Aeroway <5 X
Amenity 7 942 X X X X X X X X
Barrier 1 387
Boundary 1 454
Building 141 420 X X X X X
Craft <5X
Emergency 1 506 X
Geological <5 X
Highway 65 194 X
Historic <5 X
Landuse 10 000 X
Leisure 1 813 X X
Man_made 209 X
Military <5
Natural 4 036 X XXX
Office <5XX
Places 647
Power 18 277
Public_transport 3 199 X
Railway 5 393 X
Route 10 904 XX
Shop <5XX
Sport 657 X
Tourism <5X XXX
Waterway 1 874 X
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Hopf Energy Informatics (2018) 1:4 Page 11 of 21
Table 1 Overview to the definition of GOCs based on primary and secondary map features in the OSM database (Continued)
OSM map feature # objects Building Land use ♠Public Institution Business Food Transportation Recreation Culture Sights Countryside Road system
Secondary Map Features
Bus 1 170 X
Bicycle 3 128 X
Cuisine 268 X
Cycleway 591 XX
Denomination 210 X X
Horse 410 X
Parking 805 X
Recycling 584
Religion 502 X X
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Hopf Energy Informatics (2018) 1:4 Page 12 of 21
The major issue regarding the use of real estate portal data is the sparsity of data (i.e.,
high number of missing values). There are two reasons for missing values in features from
that data source. First, the number of real estate advertisements in the surrounding of a
target address is strongly dependent on the location (there might be, for example, more
advertisments in city centers than in suburbs) and on the point in time when the data is
retrieved (e.g., more offers at the end of school years and less offers at the start of courses
in university cities). Second, the amount of information provided in advertisments varies
to a large extent, because users only insert information on household properties that
exist (e.g., elevator in the house, pool in the garden) or do not want to provide some
information.
For a sample of 4,521 household locations in Switzerland, 8,341 advertisements from a
Swiss real estate portal with 133 different attributes have been downloaded with a web-
crawler between March 23 and May 10, 2017. Thereby, all advertisments within a radius
of 1,000 maround each household address were considered. Figure 3illustrates the avail-
ability of advertisements for the considered locations. For the majority of advertisements,
only basic attributes are present (e.g., living area, rent per month, rent per month, tex-
tual description). Table 2shows the most frequent attributes that are present in more
than 50% of the advertisments. Infrequent attributes, only avaliable in less than 1,000
advertisments, were discarded to not distort the analysis and being able to obtain gener-
alizable results in this exploratve study. Future studies may try to impute values based on
further data (Saar-Tsechansky and Provost 2007), or infer the information from textual
descriptions using text mining.
The feature values from this data source are then calculated by aggregating the data
from all advertisments within a radius of 1,000 maround each household. In the case
of numeric values, the arithmetic mean, in the case of logical values (e.g., existence of
cellar or parking space), the relative frequency, and in the case of categorical variables, the
relative frequency for each category is calculated.
Data Analysis and Results
In this section, results from the application of two VGI portals (OSM and data from real
estate advertisements) in household classification are presented. A dataset on utility cus-
tomers was available for this study. The experimental data is described below. Thereafter,
details on the implemented machine learning algorithm are given and results from the
evaluation are presented. Finally, a brief discussion of the results is given.
Fig. 3 Frequencies of real estate advertisements within a radius of 1000 maround n=4, 521 households in
the available utility dataset
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Hopf Energy Informatics (2018) 1:4 Page 13 of 21
Table 2 Data in a Swiss real estate advertisement portal: list of variables with less than 50% missing
values
Attribute Data type Missing values (in %)
Description Text 1.39
Main dwelling category Categorical (e.g., house, dwelling, business) 2.01
Type of dwelling Categorical (e.g., studio, single-family home,
shared flat)
2.01
Geographic location Coordinates 22.35
Amount rooms Number 24.13
Living area Number 30.64
Floor Categorical (e.g., 1st, 2nd, attic) 32.77
Rent per month Number 36.14
Area living flat Number 42.86
Additional costs per month Number 49.23
Net rent per month Number 49.27
Experimental data
To evaluate the proposed features from VGI data, a machine learning procedure is used.
As data for training and test, information on residential customers from a Swiss util-
ity company is available. The dataset (A) encompasses annual electricity consumption
between 2009 - 2012 and address data on 3,905 customers. For all customers in A,
information on household properties is known (see Fig. 3). The data was obtained with
online-surveys on a customer engagement web portal to increase households energy-
efficiency, maintained by our research partner BEN Energy AG, Zurich. All users gave
their consent for data processing and analytics in an anonymized way.
Using the address information, geographic features from OSM were calculated
using the offered Application Programming Interface (API) following the methodology
described in Geographic features from OpenStreetMap data section. Features from real
estate advertisments were computed as described in Feature extraction from real estate
portals section. For the analysis in this paper, 34 features from the real estate portal data
were present in more than 1000 advertisments and therefore used (the features include
for example: category and type of the dwelling, rent per month, net rent per square meter,
number of rooms, living area, year of construction, distances to next bus station / high-
way / kindergarden / school / shopping, existence of balcony, cable TV). Missing values
have been encoded with −1 being able to use the data in the further analysis.
Only for half of the customers (n=1,718), a sufficient number of five real estate adver-
tisements was available (see Fig 3). This sample of customer data with a sufficient number
of real estate advertisements B⊂Ais used for the remaining analysis.
IassumethatthesampleofcustomersinBis representative for utility customers and
find evidence for that in the statistics presented in Table 3. The distribution of household
properties are quite equal in both datasets, except the size and type of the residency:
sample Bcontains less larger dwellings and more apartments. By looking at the electricity
consumption, there is a difference in the means of both datasets: M=12.24 kWh (SD =
6.85) in Band M=13.03 kWh (SD =7.02) in A. The difference mainly results from
the fact that smaller homes and less houses are present in B. The electricity consumption
of large homes with living ares above 145 m2show for example no significant difference
(t(282)=−1.347, p>0.10). Therefore, one can assume that the sample is representative.
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Hopf Energy Informatics (2018) 1:4 Page 14 of 21
Table 3 Household properties used to evaluate the household classification methodology; the
relative class sizes in the complete sample and the finally used part, such as the mean and standard
deviation of Accuracy and F1-scores for the Random Forest model based on all data sources are given
Property Class labels Class size Class size Accuracy (%) F1-Score (%)
ABMean (SD) Mean (SD)
Household type apartment 52.03% 69.23% 77.94 (5.30)83.28 (3.01)
house 47.97% 30.77% 66.36 (13.18)
Living area ≤95 34.07% 34.95% 59.78 (2.50)65.00 (4.28)
96 −145 33.23% 40.61% 58.17 (4.10)
>145 32.70% 24.43% 56.40 (5.24)
Number of 1 person 13.07% 17.31% 58.08 (5.07)50.39 (11.97)
Residents 2 persons 40.32% 43.01% 59.76 (5.03)
3−5 persons 43.92% 38.12% 59.07 (6.09)
>5 persons 2.69% 1.47% 0 (0.00)
Heating type electric 12.86% 9.73% 90.05 (0.52)68.08 (3.59)
other 87.14% 90.27% 71.68 (4.30)
Water heating electric 50.49% 44.97% 70.03 (3.87)68.08 (3.59)
type other 49.51% 55.03% 71.68 (4.30)
Classification results
The household classification methodology, depicted in Fig. 2, is applied using the Random
Forest classifier with its implementation in the package ’randomForest’ (Liaw and Wiener
2015) for the statistical programming environment GNU R3. No automatic feature selec-
tion is done, since the Random Forest algorithm has a good internal ability to weight the
importance of certain features. The aim of this study is to investigate, how well household
characteristics can be recognized from different data sources. Following this explorative
scope, explicitly neither a tuning of the Random Forest algorithm parameters nor a model
selection (including test of other classifiers) is performed. Initially, a number of 500 trees
is used (this is the standard value of this main parameter to control the algorithm in the
used implementation) and a sensitivity analysis on how different values of this parameter
affecttheresultsisconducted.
The classification performance is evaluated using AUC as a well-known metric assess-
ing the performance of classification models (Han et al. 2012). The metric quantifies
the classification performance from 0 (lowest) to 1 (best), where 0.5 is considered as
random classification. So, all values that are clearly higher than 0.5 or lower values are
acceptable. For comparison with other studies and for better interpretation of the results,
Accuracy and the harmonic mean of Precision and Recall, called F1-score (Hastie et al.
2009,chap.7),isincludedinTable3. To obtain a solid estimation of the classification
performance, 10-fold cross-validation is applied. In this statistical technique, the data is
randomly split into k=10 independent parts (so-called folds). Training and test is per-
formed ktimes while in each iteration, one fold is used for test and the remaining folds
for training. It is generally acknowledged that this technique gives a proper estimate of
the classification performance, even with small datasets. In fact, the procedure even leads
to a conservative estimate of the performance, given the fact that cross-validation slightly
overestimates the variance of prediction results (Arlot and Celisse 2010). The classifica-
tion performance results are presented in Fig. 4together with 95%-confidence interval
obtained using the Student’s t-Distribution (df =9).
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Hopf Energy Informatics (2018) 1:4 Page 15 of 21
Fig. 4 Classification results (AUC) for different data sources with 95% confidence interval obtained using
10-fold cross-validation
One can see that all predictions made by the presented classification model are sig-
nificantly better than random (AUC >0.5). Besides that, OSM data improves the
classification performance significantly. This result replicates the findings from previous
studies (Hopf et al. 2016;Hopfetal.2017). The additional use of real estate portal data
shows small improvements for the properties space and water heating, living area and the
class >5 persons. Especially the small class can be better recognized using the real estate
portals.
On average, the use of both geographic data sources improved the classification per-
formance in AUC by 12.8% (the improvements range between 8% for single households
and 22% for dwellings >145 m2), but especially the household type could be improved by
16.6% in both classes.
A very interesting result is that classification only based on OSM data can also lead to
good results. The household type, for example, can be predicted better than using elec-
tricity consumption features. However, the prediction of household classes solely with
OSM features leads to 14.19% lower AUC on average for all properties.
The results presented in Fig. 4are obtained using 500 trees in the Random Forest
algorithm. This is a typical parameter value and the standard in the used implemen-
tation. In an empirical study involving 29 datasets, Oshiro et al. (2012)documentthat
larger numbers of trees do not significantly improve the classification performance, oth-
erwise lower numbers of trees decreease the classification quality. To assess the stabilit
of the models in this study and get an impression on possible improvements to the pre-
sented approach, a sensitivity analysis for this important parameter is conducted using
n={10, 100, 250, 500, 1000}trees for the classification using all data available in this
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Hopf Energy Informatics (2018) 1:4 Page 16 of 21
study. The results are shown in Fig. 5as point estimates with a 95% confidence interval
obtained using the t-Distribution from 10-fold cross-validation. For all binary properties
(household and heating types) only one class is shown, because AUC results for the other
classes are equal in each property. It is clear, that a low number of 10 trees leads to con-
siderably lower classification performance. However, the model cannot be significantly
improved by changing the number of trees between the values n={100, 250, 500, 1000}.
Thus, the standard value of 500 trees is quite a good choice in this presented case.
Discussion
The results from this study show that it is possible to considerably improve house-
hold classification models based on annual electricity consumption data by using freely
available VGI data. Thus, the stated research question can be answered positively.
The results are in line with previous studies on factors that influence residential electric
load (Kavousian et al. 2013; Heiple and Sailor 2008), which identify geographic data and
household details as good explanatory variables for the energy consumption in house-
holds. So it is reasonable that geographic information – together with annual electricity
consumption or alone – can serve as predictor for relevant details about residential
energy customers, as shown in this paper. The real estate portal data has, despite the
large amount of building related information contained, not helped much to increase the
predictive quality.
Besides that, the presented results confirm also findings from a previous study with a
similar household classification approach that was trained and validated with data from
Switzerland and Germany, where also the transferrability of trained models (i.e., trainig
of a model in one region and then applying this model to customers located in another
country) was positively tested (Hopf et al. 2017).
Conclusion and Implications
The phenomenon of VGI, first documented by Goodchild (2007), has created a consider-
able amount of data in the recent years that is freely available to everybody. Although this
data can lead to valuable insights, it’s application in energy data analytics is sparse.
Fig. 5 Classification results (AUC) for different number of trees in the Random Forest algorihm
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Hopf Energy Informatics (2018) 1:4 Page 17 of 21
In this paper, an overview to VGI data from an energy industry viewpoint is presented.
Besides that, it was shown, how the heterogeneous data can be transformed and finally be
used in predictive models for the specific case of household classification. Such methods
are able to predict characteristics for individual private households, such as household
type and size, heating type and number of residents. This information can be used
for value-added services, for improving the customer communication, tailoring energy-
feedback for a more energy-aware behavior, or perform targeted one-to-one marketing
campaigns (Tiefenbeck 2017).
The results presented in this paper highlight that VGI data sources are of value for pre-
dictive data analytics in the field of energy retail. So, VGI data is able to improve the
classification quality in the presented case significantly by 12.8% on average over all con-
sidered household classes. All investigated household properties can also be predicted
by just using geographic data without any further electricity consumption information,
albeit with a loss in AUC of 14.19% compared to the model using all features in this study.
Thus, the application of available crowd-sourced VGI data together with state of the art
machine learning algorithms can serve as a blueprint for further application in business
intelligence and analytics.
Even with annual electricity consumption data and location information (both is avail-
able to energy utilities for billing purposes), energy retailers and energy service providers
can start right now to predict detailed information on their customers using machine
learning and existing VGI data. So, they can enhance their customer communication with
personalized data at low cost.
Limitations
Findings in this paper are drawn from a dataset on utility customers from Switzerland.
Thus, the concrete classification performance results may vary in other studies, but
the performance gain from VGI should be replicable. In a recent study it could be, for
instance, shown that household classification models can be trained with data from one
country and applied to another one (Hopf et al. 2017). Due to the sparse number of real
estate advertisements, not the complete number of households could be used in the study.
The collection of further real estate advertisements, and also data from additional VGI
data sources, would give a more comprehensive picture of the topic.
A possible limitation to the work is the aspect of privacy preservation and user accep-
tance of predictive information systems. Krishnamurti et al. (2012) and Mah et al. (2012)
have studied the acceptance of end users for smart grid applications empirically and came
to the conclusion that private customers have a positive attitude towards smart grid appli-
cations (consumption feedback, dynamic prices, etc.). However, it is necessary that utility
companies educate their customers on the added value of smart grid technology and the
transparency of data processing (Gangale et al. 2013)inordertoplacesmartgridser-
vices well in the market. An important driver of customer acceptance are also financial
incentives such as dynamic pricing of energy that are made possible by smart meters
(Motsch 2012).
Future work
The phenomenon of VGI should be subject to future research. For instance, the persis-
tence and reliability of VGI initiatives can be investigated considering knwoledge on the
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Hopf Energy Informatics (2018) 1:4 Page 18 of 21
motivational factors and contributor behaviors in free/libre open source software projects
(Stewart and Ammeter 2002;Crowstonetal.2007;Crowstonetal.2008), as a starting
point. Besides that, other VGI data sources may be tested for their contribution in energy
data analytics applications, especially data sources that contain event and environmental
data (adding a time-dimension), such as social networks.
Considering the data quality of VGI data, the empirical definition of new features can
help to overcome issues of data sparsity and variety. This is a critical issue and became
visible in this paper with the large number of missing values in real estate advertisments.
Possible methods to overcome the problem without discarding instances or feature vec-
tors include the imputation of values (Saar-Tsechansky and Provost 2007), the inference
of values from textual descriptions using text mining, or by treating all missing values in a
vector as a separate class in model trainig and prediction. The application and comparison
of such approaches is motivated to be a subject of future research.
Regarding the household classification artifact, the prediction models can be further
enhanced by testing other parameter values for the Random Forest algorithm, bench-
mark additional algorithms with different parameter combinations, and automatic feature
selection.
Finally, it would be interesting to investigate whether VGI data sources have also value
for other topics in the field of Energy Informatics, such as load forecasting or studies
regarding consumer behavior in energy markets.
Endnotes
1The information has been collected between November 2016 and April 2017.
2listed at http://wiki.openstreetmap.org/wiki/Map_Features, last accessed March 01,
2018
3R version: 3.4.2; ’randomForest’ package version 4.6-12
4https://cran.r-project.org/, last accessed 13.01.2018
Abbreviations
AGI: Ambient geospatial information; API: Application programming interface; AUC: Area under ROC curve; CGI:
Contributed geographic information; GOC: Geographic object category; GOC: Geographic object categories; OSM:
OpenStreetMap; POI: Point of interest; POI: Points of interest; ROC: Receiver operating characteristic; RQ:Research
question; SMD: Smart meter data; SVM: Support vector machine; VGI: Volunteered geographic information; WGS84:
World geodetic system 84; XML: Extensible markup language
Acknowledgments
I thank Prof. Dr. Thorsten Staake and Prof. Dr. Hans-Arno Jacobsen, such as the faculty of 8th PhD Workshop “Energy
Informatics" in Lugano 2017 for insightful comments and feedback to earlier versions of this manuscript. My thank also
goes to the anonymous reviewers of this journal, for their valuable comments and suggestions. Besides that, I thank BEN
Energy AG (Switzerland) for providing anonymized utility data for the analysis, Rolf Fakesch and Martin Bullin for their
support in acquisition of real estate portal data, such as Philipp Stiber-Amend and André Fiedler for their help in
collecting data on VGI initiatives.
Funding
Financial supported from Eureka member countries and European Union (EUROSTARS Grant number E!9859 - BENgine II)
is greatfully acknowledged.
Availability of data and materials
Due to its nature, VGI data is available to the public and can be retrieved from the respective portals. All computational
methods used are open source and available via the Comprehensive R Archive Network4. Other materials are referenced
in this paper. The utility data used in this study cannot be published, because it contains confidential information
(address data and electricity consumption).
Author’s contribution
The author conducted the analysis and has written the manuscript. The author read and approved the final manuscript
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Hopf Energy Informatics (2018) 1:4 Page 19 of 21
Competing interests
The author declares that he has no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Received: 15 January 2018 Accepted: 20 March 2018
References
Abbasi A, Sarker S, Chiang R (2016) Big Data Research in Information Systems: Toward an Inclusive Research Agenda.
J Assoc Inf Syst 17(2):00026
Albert A, Rajagopal R (2013) Smart Meter Driven Segmentation: What Your Consumption Says About You. IEEE Trans
Power Syst 28(4):4019–4030
Anhorn J, Herfort B, Albuquerque JPd (2016) Crowdsourced validation and updating of dynamic features in
OpenStreetMap an analysis of shelter mapping after the 2015 Nepal, earthquake. In: Proceedings of the ISCRAM, 2016
Conference – Rio de Janeiro, Brazil, Rio de Janeiro. http://www.iscram2016.nce.ufrj.br/. Accessed 30 Apr 2016
Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Statist Surv 4:40–79. https://doi.org/
10.1214/09-SS054
Ballatore A, Bertolotto M, Wilson D (2012) Geographic knowledge extraction and semantic similarity in OpenStreetMap.
Knowledge and Information Systems 37(1):61–81
Ballatore A, Wilson DC, Bertolotto M (2013) A survey of volunteered open geo-knowledge bases in the semantic web. In:
Quality issues in the management of web information, Springer. pp 93–120
Beckel C, Sadamori L, Santini S (2012) Towards automatic classification of private households using electricity
consumption data. In: Pappas GJ (ed). Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for
Energy-Efficiency in Buildings. ACM, Toronto and Ontario. pp 169–176
Beckel C, Sadamori L, Santini S (2013) Automatic socio-economic classification of households using electricity
consumption data. In: Culler D, Rosenberg C (eds). Proceedings of the Fourth International Conference on Future
Energy Systems. Berkeley and California, ACM. pp 75–86
Beckel C, Sadamori L, Staake T, Santini S (2014) Revealing household characteristics from smart meter data. Energy
78:397–410
Becker M (2012) Geodesy. In: Springer Handbook of Geographic Information. Springer, Berlin, Heidelberg. pp 95–117
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28. 00276
Chicco G (2012) Overview and performance assessment of the clustering methods for electrical load pattern grouping
Vol. 42. pp 68–80
Constantiou ID, Kallinikos J (2015) New games, new rules: big data and the changing context of strategy. J Inf Technol
30(1):44–57
Crowston K, Li Q, Wei K, Eseryel UY, Howison J (2007) Self-organization of teams for free/libre open source software
development. Inf Softw Technol 49(6):564–575. 00195
Crowston K, Wei K, Howison J, Wiggins A (2008) Free/Libre Open-source Software Development: What We Know and
What We Do Not Know. ACM Comput Surv 44(2):7:1–7:35. 00330
Dangerman ATCJ, Schellnhuber HJ (2013) Energy systems transformation. Proc Natl Acad Sci 110(7):E549–E558
Elwood S, Goodchild MF, Sui DZ (2012) Researching Volunteered Geographic Information: Spatial, Data, Geographic
Research, and New Social Practice. Ann Assoc Am Geogr 102(3):571–590
Eurostat (2017) Final consumption expenditure of households, by consumption purpose - Eurostat (Code: tsdpc520, Last
update: 25/01/17). http://ec.europa.eu/eurostat/web/products-datasets/- /tsdpc520. Accessed 25 June 2017
Eysenbach G (2008) Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness. J Med
Internet Res 10(3). https://doi.org/10.2196/jmir.1030
Fei H, Kim Y, Sahu S, Naphade M, Mamidipalli SK, Hutchinson J (2013) Heat Pump Detection from Coarse Grained Smart
Meter Data with Positive and Unlabeled Learning. In: Proceedings of the 19th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, KDD ’13. ACM, New York. pp 1330–1338
Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world
classification problems? J Mach Learn Res 15(1):3133–3181
Gangale F, Mengolini A, Onyeji I (2013) Consumer engagement: An insight from smart grid projects in Europe. Energy
Policy 60:621–628. 00058
Gebauer H, Worch H, Truffer B (2014) Value Innovations in Electricity Utilities. In: Rønning R, Enquist B, Fuglsang L (eds).
Framing Innovation in Public Service Sectors, Vol. 30. Routledge Studies in Innovation, Organization and Technology,
Routledge. p 85ff
Gillon K, Brynjolfsson E, Mithas S, Griffin J, Gupta M (2012) Business Analytics: Radical Shift or Incremental Change? In: ICIS,
2012 Proceedings. AIS electronic library. ISBN: 978-0-615-71843-9. http://aisel.aisnet.org/icis2012/proceedings/
Panels/4/
Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Guyon I, Elisseeff A (2006) An Introduction to Feature Extraction. In: Guyon I, Nikravesh M, Gunn S, Zadeh L (eds). Feature
Extraction, Vol. 207 of Studies in Fuzziness and Soft, Computing. Springer, Berlin, Heidelberg
Han J, Kamber M, Pei J (2012) Data mining: Concepts and techniques, The Morgan Kaufmann, series in data management
systems, 3. edn. Elsevier, Amsterdam
Harvey F (2013) To Volunteer or to Contribute Locational Information? Towards Truth in Labeling for Crowdsourced
Geographic, Information. In: Crowdsourcing Geographic Knowledge. Springer, Dordrecht. pp 31–42
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Hopf Energy Informatics (2018) 1:4 Page 20 of 21
Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning, Springer Series in Statistics. Springer New
York, New York
Haworth B, Bruce E (2015) A Review of Volunteered Geographic Information for Disaster Management. Geogr Compass
9(5):237–250
Heiple S, Sailor DJ (2008) Using building energy simulation and geospatial modeling techniques to determine high
resolution building sector energy consumption profiles. Energy Build 40(8):1426–1436
Hopf K, Riechel S, Sodenkamp M, Staake T (2017) Predictive Customer Data Analytics – The Value of Public Statistical Data
and the Geographic Model Transferability. In: Proceedings of the 38. International Conference on Information
Systems (ICIS). AIS electronic library, Seoul
Hopf K, Sodenkamp M, Kozlovskiy I (2016) Energy data analytics for improved residential service quality and energy
efficiency. In: Proceedings of the 24. European Conference on Information Systems (ECIS). AIS electronic library,
Istanbul. http://aisel.aisnet.org/ecis2016_rip/73/
Hopf K, Sodenkamp M, Kozlovskiy I, Staake T (2016) Feature extraction and filtering for household classification based on
smart electricity meter data. In: Computer Science-Research and Development, Vol. (31) 3. Springer Berlin Heidelberg,
Zürich. pp 141–148
Hopf K, Sodenkamp M, Staake T (2018) Enhancing energy efficiency in the residential sector with smart meter data
analytics. forthcoming, https://doi.org/10.1007/s12525-018- 0290-9
Horita FEA, Degrossi LC, de Assis LFG, Zipf A, de Albuquerque JP (2013) The use of volunteered geographic information
(VGI) and crowdsourcing in disaster management: a systematic literature review. In: Proceedings of the 19. Americas
Conference on Information Systems (AMCIS) 2013, Chicago, Illinois. AIS electronic library. https://aisel.aisnet.org/
amcis2013/eGovernment/GeneralPresentations/4/
Hua J, Tembe WD, Dougherty ER (2009) Performance of feature-selection methods in the classification of high-dimension
data. Pattern Recog 42(3):409–424
Janowicz K, Raubal M, Kuhn W (2011) The semantics of similarity in geographic information retrieval. J Spat Inf Sci
2011(2):29–57
(2015) OpenStreetMap in GIScience, Lecture Notes in Geoinformation and Cartography. In: Jokar Arsanjani J, Zipf A,
Mooney P, Helbich M (eds). Springer International Publishing, Cham
Kavousian A, Rajagopal R, Fischer M (2013) Determinants of residential electricity consumption: Using smart meter data to
examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior. Energy 55:184–194
Keogh E, Mueen A (2011) Curse of Dimensionality. In: Sammut C, Webb GI (eds). Encyclopedia of Machine Learning.
Springer, Boston. pp 257–258
Kozlovskiy I, Sodenkamp M, Hopf K, Staake T (2016) Energy informatics for environmental, economic and social
sustainability: A case of the large-scale detection of households with old heating systems. In: Proceedings of the 24.
European Conference on Information Systems (ECIS). AIS electronic library, Istanbul
Krishnamurti T, Schwartz D, Davis A, Fischhoff B, de Bruin WB, Lave L, Wang J (2012) Preparing for smart grid technologies:
A behavioral decision research approach to understanding consumer expectations about smart meters. Energy
Policy 41:790–797. 00084
Kudo M, Sklansky J (2000) Comparison of Algorithms that Select Features for Pattern Classifiers. Pattern Recogn
33(1):25–41. 00931
Kwac J, Tan C-W, Sintov N, Flora J, Rajagopal R (2013) Utility customer segmentation based on smart meter data:
Empirical study. In: Smart Grid Communications (SmartGridComm) 2013 IEEE, International Conference on. IEEE,
Vancouver. pp 720–725. https://doi.org/10.1109/SmartGridComm.2013.6688044
Liaw A, Wiener M (2015) randomForest: Breiman and Cutler’s Random Forests for Classification and Regression. Fortran
original by Leo Breiman and Adele Cutler. https://cran.r-project.org/web/packages/randomForest/index.html.
Accessed 25 Oct 2017
Liu H, Motoda H (eds) (2008) Computational methods of feature selection, Chapman & Hall/CRC data mining and
knowledge discovery series. Chapman & Hall/CRC, Boca Raton
Mah DN-y, van der Vleuten JM, Hills P, Tao J (2012) Consumer perceptions of smart grid development: Results of a Hong
Kong survey and policy implications. Energy Policy 49:204–216. 00063
Markard J, Truffer B (2006) Innovation processes in large technical systems: Market, liberalization as a driver for radical
change?. Research Policy 35(5):609–625. 00175
McLoughlin F (2013) Characterising Domestic Electricity Demand for Customer, Load Profile Segmentation, PhD thesis.
Dublin Institute of Technology. http://arrow.dit.ie/engdoc/62
Mithas S, Lee MR, Earley S, Murugesan S, Djavanshir R (2013) Leveraging Big Data and Business Analytics [Guest editors’
introduction]. IT Prof 15(6):18–20
Müller O, Junglas I, Brocke Jv, Debortoli S (2016) Utilizing big data analytics for information systems research: challenges,
promises and guidelines. Eur J Inf Syst 25(4):289–302
Mondzech J, Sester M (2011) Quality Analysis of OpenStreetMap Data Based on Application, Needs. Cartographica Int J
Geogr Inf Geovisualization 46(2):115–125
Mooney P, Corcoran P, Ciepluch B (2013) The potential for using volunteered geographic information in pervasive health
computing applications. J Ambient Intell Humanized Comput 4(6):731–745
Motsch W (2012) Dynamische Tarife zur Kundeninteraktion mit einem Smart Grid. Vieweg+Teubner Verlag, Wiesbaden
Oshiro TM, Perez PS, Baranauskas JA (2012) How many trees in a random forest?. In: Perner P (ed). Machine Learning and
Data Mining in Pattern Recognition. Springer Berlin, Heidelberg. pp 154–168
Rinner C, Fast V (2015) A Classification of User Contributions on the Participatory Geoweb. In: Harvey F, Leung Y (eds).
Advances in Spatial Data Handling and Analysis, Advances in Geographic, Information Science. Springer International
Publishing, Cham. pp 35–49
Saar-Tsechansky M, Provost F (2007) Handling missing values when applying classification models. J Mach Learn Res
8(Jul):1623–1657
Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics
23(19):2507–2517
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Hopf Energy Informatics (2018) 1:4 Page 21 of 21
Schwering A (2008) Approaches to Semantic Similarity Measurement for Geo-Spatial Data: A Survey. Trans GIS 12(1):5–29
See L, Mooney P, Foody G, Bastin L, Comber A, Estima J, Fritz S, Kerle N, Jiang B, Laakso M, Liu H-Y, Milˇ
cinski G, Nikšiˇ
cM,
Painho M, P˝
odör A, Olteanu-Raimond A-M, Rutzinger M (2016) Crowdsourcing, Citizen Science or Volunteered
Geographic Information? The Current State of Crowdsourced Geographic Information. ISPRS Int J Geo-Inf 5(5):55
Sester M, Arsanjani JJ, Klammer R, Burghardt D, Haunert J-H (2014) Integrating and Generalising Volunteered Geographic
Information. In: Burghardt D, Duchêne C, Mackaness W (eds). Abstracting Geographic Information in a Data Rich,
World, Lecture Notes in Geoinformation and Cartography. Springer International Publishing. pp 119–155
Sharma R, Mithas S, Kankanhalli A (2014) Transforming decision-making processes: a research agenda for understanding
the impact of business analytics on organisations. Eur J Inf Syst 23(4):433–441
Sodenkamp M, Kozlovskiy I, Hopf K, Staake T (2017) Smart Meter Data Analytics for Enhanced Energy Efficiency in the
Residential Sector. In: Wirtschaftsinformatik 2017 Proceedings. AIS electronic library, St. Gallen
Stefanidis A, Crooks A, Radzikowski J (2013) Harvesting ambient geospatial information from social media feeds.
GeoJournal 78(2):319–338. 00212
Stewart K, Ammeter T (2002) An exploratory study of factors influencing the level of vitality and popularity of open
source projects. In: ICIS 2002 Proceedings. AIS electronic library
Tiefenbeck V (2017) Bring behaviour into the digital transformation. Nat Energy 2:17085
Verma A, Asadi A, Yang K, Tyagi S (2015) A data-driven approach to identify households with plug-in electrical vehicles
(PEVs). Appl Energy 160:71–79
Zeifman M (2014) Smart meter data analytics: Prediction of enrollment in residential energy efficiency programs. In: 2014
IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE. pp 413–416. 00007. https://doi.org/10.
1109/TCE.2011.5735484. ISSN 0098-3063
Zhou K, Fu C, Yang S (2016) Big data driven smart energy management: From big data to big insights. Renewable and
Sustainable Energy Reviews 56:215–225. 00052
Zook M, Graham M, Shelton T, Gorman S (2010) Volunteered Geographic Information and Crowdsourcing Disaster Relief:
A Case Study of the Haitian Earthquake. In: SSRN Scholarly Paper ID 2216649. Social Science Research Network,
Rochester. http://papers.ssrn.com/abstract=2216649
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