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Information systems are most often the main focus when considering applications of Big Data technology. However, the energy domain is more than suitable also given the worldwide coverage of electrification. Additionally, the energy sector has been recognized to be in dire need of modernization, which would include tackling (i.e. processing, storing and interpreting) a vast amount of data. The motivation for including a case study on the applications of big data technologies in the energy domain is clear, and is thus the purpose of this chapter. An application of linked data and post-processing energy data has been covered, whilst a special focus has been put on the analytical services involved, concrete methodologies and their exploitation.
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Chapter 10
Case Study from the Energy Domain
Dea Puji´c(B
), Marko Jeli´c, Nikola Tomaˇsevi´c, and Marko Bati´c
Institute Mihajlo Pupin, University of Belgrade, Belgrade, Serbia
dea.pujic@pupin.rs
Abstract. Information systems are most often the main focus when
considering applications of Big Data technology. However, the energy
domain is more than suitable also given the worldwide coverage of elec-
trification. Additionally, the energy sector has been recognized to be in
dire need of modernization, which would include tackling (i.e. process-
ing, storing and interpreting) a vast amount of data. The motivation for
including a case study on the applications of big data technologies in the
energy domain is clear, and is thus the purpose of this chapter. An appli-
cation of linked data and post-processing energy data has been covered,
whilst a special focus has been put on the analytical services involved,
concrete methodologies and their exploitation.
1 Introduction
Big Data technologies are often used in domains where data is generated, stored
and processed at rates that cannot be efficiently processed by one computer.
One of those domains is definitely that of energy. Here, the processes of energy
generation, transmission, distribution and use have to be concurrently monitored
and analyzed in order to assure system stability without brownouts or blackouts.
The transmission systems (grids) that transport electric energy are in general
very large and robust infrastructures that are accompanied by a great deal of
monitoring equipment. Novel Internet of Things (IoT) concepts of smart and
interconnected homes are also pushing both sensors and actuators into peoples
homes. The power supply of any country is considered to be one the most crit-
ical systems and as such its stability is of utmost importance. To that effect,
a wide variety of systems are deployed for monitoring and control. Some of
these tools are presented in this chapter with a few from the perspective of end
users (Non-Intrusive Load Monitoring, Energy Conservation Measures and User
Benchmarking) and a few from the perspective of the grid (production, demand
and price forecasting).
2 Challenges Withing the Big Data Energy Domain
In order to be able to provide advanced smart grid, user-oriented services, which
will be discussed further in this chapter, integration with high volume, heteroge-
neous smart metering data (coming both from the grid side, e.g. placed in power
c
The Author(s) 2020
V. Janev et al. (Eds.): Knowledge Graphs and Big Data Processing, LNCS 12072, pp. 165–180, 2020.
https://doi.org/10.1007/978-3-030-53199-7_10
166 D. Puji´cetal.
substations, and from the user side, e.g. installed in homes and buildings) is a
prerequisite. To specify, suggest and deliver adequate services to end users (i.e.
energy consumers) with respect to their requirements and power grid status,
various forms of energy data analytics should be applied by distribution system
operators (DSO) and grid operators such as precise short- and long-term energy
production and consumption forecasting. In order to deliver such energy analyt-
ics, historical energy production data from renewable energy sources (RES) and
historical consumption data, based on smart metering at consumer premises and
LV/MV power substations, must be taken into account.
The main challenge to providing advanced smart grid services is related to
the integration and interoperability of high volume heterogeneous data sources
as well as adequate processing of the acquired data. Furthermore, making this
data interoperable, based on Linked Data API, and interlinked with other data
sources, such as weather data for renewable energy sources (RET) production
analysis, number of inhabitants per home units, etc., is essential for providing
additional efficient user tailored analytical services such as energy conservation
action suggestions, comparison with other consumers of the same type, etc.
Another challenge is related to analysis of grid operations, fault diagnostics
and detection. To provide such advanced analytics, real-time integration and
big data analysis performed upon the high volume data streams coming from
metering devices and power grid elements (e.g. switches, transformers, etc.) is
necessary, and could be solved using Linked Data principles. Finally, to sup-
port next generation technologies enabling smart grids with an increased share
of renewables, it is necessary to provide highly modular and adaptable power
grids. In addition, adequate tools for off-line analysis of power system optimal
design should be deployed. These analytical tools should also incorporate allo-
cation of optimal reconfiguration of power grid elements to provide reliable and
flexible operation as an answer to the changing operational conditions. Tools for
planning and reconfiguring power distribution networks consider power station
infrastructure and its design, number and capacity of power lines, etc. To pro-
vide such advanced grid capabilities, integration with historical power grid data,
archives of detected alarms and other relevant operational data (such as data
from smart metering, consumption data, etc.) is necessary. Therefore, the main
challenge is to provide digested input to the batch-processing, big data analytics
for power grid infrastructure planning.
Having all of this in mind, the significance of big data processing techniques
is obvious. On the other hand, further in this chapter examples of analytical
services will be presented and discussed.
3 Energy Conservation Big Data Analytical Services
Improving quality of life through advanced analytics is common nowadays in
various domains. Consequently, within the energy domain, collecting data from
numerous smart meters, processing it and drawing conclusions are common con-
cepts in the field of developing energy conversation services. The amount of
Chapter 10 Case Study from the Energy Domain 167
aforementioned data highly depends on the service’s principal use. If the focus
is put on just one household, data can be undoubtedly processed using only one
computer. Nonetheless, if the scale of a problem is a neighbourhood, munici-
pality or city level, data processing and analytical computations can be taken
as a big data problem. Therefore, within this chapter, methodologies for smart
energy services are going to be discussed.
3.1 Non-Intrusive Load Monitoring
The first of these is so-called Non-Intrusive Load Monitoring (NILM). NILM was
motivated by conclusions, such as those from [70], which claimed that up to 12% of
residential energy consumption can be decreased by giving users feedback on how
the energy has been used. In other words, by providing the user with information
about which of their appliances is using electrical energy and how much, signifi-
cant savings can be reached. Nonetheless, providing this kind of information would
require installation of numerous meters all around households, which is usually
unacceptable for the end-user. Therefore, instead of the Intrusive Load Monitor-
ing solution which influences users’ convenience, Non-Intrusive Load Monitoring
was proposed by Hart in [183] with the main goal of providing users with the same
information in a harmless way by aggregating entire household consumption at the
appliance level, which can be seen in Fig. 1.
Fig. 1. Non-Intrusive Load Monitoring concept
Having in mind the previous information, two main problems are present
within the NILM literature - classification, which provides information about
the activation on the appliance level, and regression for the estimation of the
appliance’s individual consumption, as shown in the example Fig. 2. As these are
some of the most common problems in advanced analytics, typical methodologies
168 D. Puji´cetal.
employed to address these are leading machine learning approaches, which are
going to be presented and discussed further in this section to give an example
of the use of applied big data technologies in the energy domain.
Fig. 2. NILM classification and regression example
As a first step, in this section, the currently present publicly available datasets
will be introduced as the basis of data-driven models, which will be discussed
further. Depending on the sampling rate, within the NILM literature, data and
further corresponding methodologies are usually separated in two groups - high
and low frequency ones. For high frequency, measurements with a sampling
time of less than 1 ms are considered. These kind of data are usually unavailable
in everyday practice due to the fact that usual residential metering equipment
has a sampling period around 1 s and is put as the low frequency group. This
difference in sampling rate further influences the choice of the disaggregation
methodology and preprocessing approach for the real-time coming data used as
the corresponding inputs.
When discussing publicly available data sets, methodologies are not strictly
separated in accordance with the chosen sampling rate but rather by the geo-
graphical location. In other words, measurements usually correspond to some
localized neighbourhood from which both high and low frequency data might
be found in the same data set. The first published dataset we refer to is REDD
(Reference Energy Disaggregation Data Set, 2011) [256]. It includes both low
and high sampling frequency measurements from six homes in the USA. For the
first group, both individual and aggregated power measurements were covered
Chapter 10 Case Study from the Energy Domain 169
for 16 different appliances, allowing the development of various models, which
require labeled data. By contrast, high frequency measurements contain only
aggregated data from the household, so the developers have to use unsupervised
techniques. Another widely spread and used data set published with [238]is
UK-DALE (UK Domestic Appliance-Level Electricity) collected in the United
Kingdom from five houses. It, again, covers the whole range of sampling rates,
and, similarly to REDD, contains labeled data only for those with a sampling
period bigger than 1 s. Additional data sets that should be addressed are REFIT
[318], ECO (Electricity Consumption and Occupancy) [33], IHEPCDS (Individ-
ual household electric power consumption Data Set) [319] for low sampling rate
and BLUED [137]andPLAID[145] for the high one1.
After presenting the available data, potential and common problems with
data processing as part of the theme of big data will be discussed. The first one,
present in most of the data sets, is the presence of the missing data. Depending
on the data set and the specific household appliance, the scale of this problem
varies. For example, in the case of refrigerators, this is a minor problem which
can be neglected because it works circularly, so each approximately 20 min it
turns on or off, leading to numerous examples of both active and inactive work-
ing periods. By contrast, when, for example, a washing machine is considered,
dropping down the sequence of its activation is unacceptable as it is turned on
twice a week in a household on average, so it is difficult to collect enough data
for training purposes. Therefore, different techniques were adapted in different
papers for additional data synthesization from simply adding existing individual
measurements of the appliance’s consumption on the aggregated power measure-
ments in some intervals when the considered appliance has not been working to
more sophisticated approaches such as generative modeling, which was used to
enrich data from commercial sector measurements [193].
It is worth mentioning here that characteristics of the data from these differ-
ent sets significantly deviate in some aspects as a result of differences in location,
habits, choice of domestic appliance, number of occupants, the average age of
the occupant etc. The NILM literature has attempted to address this general-
ization problem. Even though the problem of achieving as high performance
as possible on the testing rather than training domain is a hot topic in many
fields of research within Machine Learning (ML) and Big Data, the generaliza-
tion problem is even more crucial for NILM. As different houses might include
different types of the same appliances, the performance on the data coming from
the house whose measurements have not been used in the training process might
be significantly lower than the estimated one. Additionally, it is obvious that the
only application of the NILM models would be in houses which have not been
used in the training phase, as they do not have labeled data (otherwise, there
would be no need for NILM). Bearing all of this in mind, validating the results
from the data coming from the house whose measurements have already been
used in the training process is considered inadequate. Thus, it is accepted that
for validation and testing purposes one, so called, unseen house is set aside and
1http://wiki.nilm.eu/datasets.html.
170 D. Puji´cetal.
all further validation and testing is done for that specific house. Nonetheless, the
houses covered by some publicly available dataset are by the rule in the same
neighbourhood, which leads to the fact that data-driven models learn patterns
which are characteristics of the domain rather than the problem. Therefore, sep-
aration of the house from the same dataset might be adequate. Finally, the last
option would be validating and testing the measurements from the house using
a different data set.
State-of-the-art NILM methodologies will be presented later in this section
alongside corresponding estimated performance evaluations. Historically, the
first ones were Hidden Markov Models and their advancements. They were
designed to model the processes with unobservable states, which is indeed the
case with the NILM problem. In other words, the goal is to estimate individual
consumption in accordance with the observable output (aggregated consump-
tion). This approach and its improvements have been exploited in numerous
papers such as [227,245,255,293,294], and [56]. However, in all of the previously
listed papers which cover the application of numerous HMM advancements to the
NILM problem, the problem of error propagation is present. Namely, as HMM
presumes that a current state depends on a previous one, mistakes in estimating
previous states have a significant influence on predicting current ones.
Apart from HMMs, there are numerous unsupervised techniques applied for
NILM. The main cause of this is the fact that labeled data for the houses in which
services are going to be installed are not available, as already discussed. There-
fore, many authors choose to use unsupervised learning techniques instead of
improving generalization on the supervised ones. Examples of these attempts are
shownin[194] where clusterization and histogram analysis has been employed
before using the conditional random fields approach, in [344] where adapta-
tion over unlabeled data has been carried out in order to improve performance
on the gaining houses, and in [136] where disaggregation was described as a
single-channel source separation problem and Non Negative Matrix Factoriza-
tion and Separation Via Tensor and Matrix Factorization were used. Most of
these approaches were compared with the HMM-based one and showed signifi-
cant improvements. Another approach to gain the best generalization capabilities
possible that can be found in the literature is semi-supervised concept in which
a combination of supervised and unsupervised learning is present. In [30], self-
training has been carried out using internal and external information in order
to decrease the necessity of labeled data. Further, [208] proposes the application
of transfer learning and blind learning, which exploits data from training and
testing houses.
Finally, supervised techniques were widely spread in the literature as well. Cur-
rently, various ML algorithms hold a prime position with regards to supervised
approaches, as they have proven themselves to be an adequate solution for the dis-
cussed problem, as reviewed in [419]. The biggest group currently popular in the
literature is neural networks (NNs). Their ability to extract complex features from
an input sequence was confirmed to increase their final prediction performance.
Namely, two groups stood out to be most frequently used - Recurrent Neural Net-
works (RNNs) with the accent on Long Short Term Memory (LSTM) [302], and
Chapter 10 Case Study from the Energy Domain 171
Convolutional Neural Networks (CNNs) with a specific subcategory of Denoising
Autoencoders [239].
After presenting various analytical approaches for solving the NILM problem,
it is crucial to finish this subsection with the conclusion that results obtained
by this service could be further post-processed and exploited. Namely, disaggre-
gated consumption at the appliance level could be utilized for developing failure
detection services in cooperation with other heterogeneous data.
3.2 Energy Conservation Measures (ECM)
When discussing the appeal and benefits of energy savings and energy con-
servation amongst end users, especially residential ones, it is no surprise that
users react most positively and vocally when potential cost savings are men-
tioned. Of course, when this is the main focus, retrofitting old technologies,
improving insulation materials, replacing windows and installing newer and more
energy-efficient technologies is usually included in the course of action first rec-
ommended. This is mainly because the aspects that are tackled by these modifi-
cations are the largest source of potential heat losses and energy conversion ineffi-
ciencies. However, there is a significant and still untapped potential for achieving
significant energy savings by correcting some aspects of user behaviour.
Besides inefficient materials, bad habits are one of the main causes of high
energy loss, especially in heating and cooling applications with the thermal
demand being a distinct issue due to the high volume of energy being spent
in the residential sector on it. Finding the crucial behavioral patterns that users
exhibit when unnecessarily wasting energy is key for efficient mitigation and,
therefore, a smart home concept is proposed in order to analyze user behavior
and facilitate the necessary changes. In order to obtain data to be able to sug-
gest energy conservation measures, a set of smart sensors should be deployed
to monitor various parameters. Some of these sensors could include but are not
limited to:
Smart external meter interfaces (measurement of total energy consumption
in real-time);
Smart electricity plugs and cables (measurement of energy consumption per
appliance in real time and possibility of on/off control);
– Smart thermostats (measurement and continuous control of reference tem-
perature and possibly consumed energy);
Occupancy sensors (measurement of occupancy and motion and ambient tem-
perature also);
Window sensors (measurements of open/close status of windows and doors
and ambient temperature also);
Volatile organic compound (VOC) sensors (measurement of air quality and
ambient temperature)
In some cases where installing smart plugs and cables is not deemed to be eco-
nomical, a NILM algorithm described in Subsect. 3.1 can be employed in order to
172 D. Puji´cetal.
infer individual appliance activity statuses using only the data from the external
meter. When widespread deployment of such sensors is being done, the amount
of data that should be collected, stored and processed quickly grows due to the
fact that multiple sensors are to be deployed in each room and that each of the
sensors usually reports multiple measurements (e.g. the window sensor reports
the temperature besides the open/close status, but also has a set of utility mea-
surements such is the network status strength, battery status, etc. which should
also be monitored as they provide crucial data regarding the health of the device
itself). Therefore, efficient solutions, possibly from the realm of big data, should
be employed in order to facilitate efficient storage and processing of data as the
problematic user behavior is time-limited and should be pointed out to the user
in due course while a problematic event is ongoing.
A small-scale use case of such a system was tested on around two dozen
apartments in the suburbs of Leers, France with the proposed architecture of
the system illustrated in Fig. 3. Using such an architecture, the back-end of the
Fig. 3. Proposed architecture of a small-scalle ECM system
system that employs a MySQL database for static data storage regarding the
apartment IDs and custom notification settings in conjunction with an ontology
for storing room layouts and detailed sensor deployment data provides support
for the main ECM engine that analyses data from the real-time IoT-optimized
NoSQL Influx database and sends push notifications to the end users notifying
them of energy-inefficient behaviour by cross-correlating different measurements
from different sensors. For example, when a heating or cooling device is observed
to be turned on in an unoccupied space, the user is warned. If the user acts upon
such information and resolves the issue, the notification is dismissed automati-
cally, or if the user does not react and the problematic event goes unresolved, he
or she is re-notified after a predefined period of time. These events are analyzed
Chapter 10 Case Study from the Energy Domain 173
with different scopes for individual rooms but also for entire apartments. Also,
since smart sensors are already deployed, the energy conservation analysis can
also be extended to regard security (no occupancy whilst a door or window is
open) and health (poor air quality and windows closed) aspects also. Of course,
each event is analyzed separately and appropriate notifications with corrective
actions are issued to the end user.
3.3 User Benchmark
Besides the most obvious motivating factor of energy savings – monetary sav-
ings – another factor that can greatly impact users’ behavior is social pres-
sure. Namely, in a hypothetical scenario where different users were placed in a
competition-like environment where the main goal is to be as energy-efficient
as possible or, in other words, where each user’s score is determined by how
efficiently they consume energy, those users would be more likely to strive to
perform better and hence consume energy in a more environmentally friendly
way. In order to facilitate such an environment, a benchmarking engine has to
be developed in order to provide an algorithm that would rank the users.
[81,113] and [329] in the literature point out that the benchmarking proce-
dures in the residential sector have long been neglected in favor of industrial
applications. Different algorithms and technologies proposed as core include:
Simple normalization
Ordinary least squares (OLS)
Stochastic frontier analysis (SFA)
Data envelopment analysis (DEA)
Simulation (model-based) rankings
Artificial neural networsk (ANNs)
Fuzzy reasoning
with related literature [171] offering several dozens of additional related algo-
rithms for multi-criteria decision making (MCDM). The applications of the afore-
mentioned algorithms found in the literature are generally focused on schools,
other public buildings and offices, with very few papers, such as [259,291]and
[461], analyzing the residential sector.
One of the most prominent standards in energy efficiency ranking is the
acclaimed Energy Star program [182], which rates buildings on a scale from
1 to 100 based on models and normalization methods of statistical analysis
performed over a database from the US Energy Information Administration
(EIA). However, the Energy Star rating does not take into account dynamic
data obtained by observing the ongoing behavior of residents. This is where the
concept of an IoT-powered smart home can provide a new dimension to energy
efficiency benchmarking through real-time analysis of incoming data on how
people use the space and appliances at their disposal.
The basis of every ranking algorithm is a set of static parameters that roughly
determines the thermal demand of the considered property. These parameters
174 D. Puji´cetal.
generally include: total heated area, total heated volume, outward wall area, wall
thickness, wall conductivity or material, number of reported tenants. This data
generally is not massive in volume and is sufficient for some elementary ranking
methods. However, an energy efficiency rating that only takes into consideration
this data would only have to be calculated once the building is constructed or
if some major renovations or retrofits are being made. As such, it would not
be able to facilitate a dynamic competition-based environment in which users
would compete on a daily or weekly basis on who is consuming their energy in
the most economical way.
Given the reasoning above, the static construction and occupancy parame-
ters are extended with a set of dynamic parameters that are inferred based on
sensor data collected by the smart home. This data could, for example, include:
total consumed energy, occupancy for the entire household, cooling and heating
degree days, responsiveness to user-tailored behavior-correcting messages, align-
ment of load with production from renewable sources, etc. As these parameters
are changing on a day-to-day basis, their dynamic nature would provide a fast-
paced source that would power the fluctuations in energy efficiency scores of
individual users and ultimately help users to see that their change in behaviour
has made an impact on their ranking. Also, it is worth mentioning that when
users within a same micro-climate are to be ranked, using heating and cool-
ing degree days may prove to be redundant as all users would have the same
parameters in this regard. Therefore, this data can be augmented using indoor
ambient temperature measurements in order to monitor overheating in winter
and overcooling in summer.
The most important procedure that should be conducted within user bench-
marking solutions in order to provide a fair comparison between different users
with different habits and daily routines is to provide a so-called normalization
of consumed energy. This means that, for example, larger consumers should not
be discriminated just based on higher consumption; rather, other factors such
as the amount of space that requires air conditioning or the number of people
using the considered space should be taken into account. In this regard, simply
dividing the total consumed energy by the, for example, heated area provides a
good first estimate of how energy-efficient different users are per unit of surface,
but also implies that a linear relation between area and energy is assumed, which
might not be their inherent relationship. In order to mitigate against this issue,
vast amounts of data should be collected from individual households using IoT
sensors and analyzed in order to either deduce appropriate relations required
for normalization or to provide a basis for the aforementioned algorithms (DEA,
SFA, etc.), which assign different weights to each of the parameters taken into
account.
4 Forecasters
Following the widespread deployment of renewable sources such as wind tur-
bines, photovolotaic panels, geothermal sources, biomass plants, solar thermal
Chapter 10 Case Study from the Energy Domain 175
collectors and others, mainly as a result of various government-enforced schemes,
programs and applicable feed-in tariffs, the stability of the grid has been signif-
icantly compromised. The integration of these novel sources has proven to be
a relatively cumbersome task due to their stochastic nature and variable pro-
duction profile, which will be covered in greater depth in Subsect.4.2. Since the
production of most of these sources is highly correlated with meteorological data
(wind turbine production with wind speed and photovoltaic production with irra-
diance and cloud coverage), legacy electrical generation capacities (coal, nuclear
and hydro power plants) which have a significantly shorter transient between
different states of power output have to balance the fast-paced variations in gen-
eration that are a byproduct of the introduction of renewable sources. Since total
generation is planned in order to be able to fulfill the total demand that will
be requested, being able to know beforehand how much energy will be required
in the future and how much energy will be available can provide a basis for
potential energy and cost savings through optimal resource planning.
4.1 Demand Forecaster
Given the importance of demand forecasting, it is expected that this topic will
be covered by more than a few authors in their published research. However,
even though there is a noticeable number of publications in this regard, the
topic of energy demand forecasting and the methods used for its estimation still
appear to be under-explored without a unified proposed approach and most of
the studies being case-specific. In that regard, a probabilistic approach for peak
demand production is analyzed in [322], an autoregressive model for intra-hour
and hourly demand in [450] and ANN-powered short-term forecasting in [401].
Short-term forecasting is also analyzed whilst making use of MARS, SVR and
ARIMA models in [9] and [463] presenting a predictive ML approach. Deep
learning frameworks are discussed by [34] and [466]. DSM in connection with
time-of-use tariffs is analyzed by [200] and simultaneous predictions of electricity
price and demand in smart grids in [314].
Some authors like [105,149,195] and [12] also discuss demand forecasting but
place the focus of their research on the predictors that can be used to predict and
correlate with the demand values. In this regard, [486] analyzes the correlation
of indoor thermal performance and energy consumption. However, again, very
few studies focus on residential users, i.e. households and apartments, especially
with regard to dynamic data that depicts the ongoing use of that household.
In line with what other authors have noted in their work, the crucial factors
that affect demand and that are to be taken into account when building predic-
tive models are the meteorological conditions of the analyzed site. In essence,
this correlation is not direct, but rather the temperature, wind speed and direc-
tion and irradiance have a significant impact on the use of heating and cooling
devices, which are usually the largest consumers of energy in residential house-
holds without district heating and cooling. Besides, the current season of the
year in moderate climates greatly determines what climatic conditions can be
expected, and, therefore, the geographic properties of the analyzed site have to
176 D. Puji´cetal.
be taken into account since it is the location that determines how severe the
seasonal variations in climatic conditions will be. As for the static data, the
total floor space or heated volume are also said to be closely correlated with
total consumption, but cannot be used to dynamically estimate demand with
high time resolution. Here is where large volumes of IoT sensor data collected
directly from homes can be of great help in increasing the precision of predictive
models. Namely, indoor ambient temperature coupled with outdoor meteoro-
logical conditions with live occupancy data in real time can provide a precise
short-term estimation of the consumption profile. Furthermore, if past behaviour
is taken into account (in the form of previous demand curves both as an average
over a larger time period in the past and the more current ones from the pre-
vious couple of days) with current day indicators (i.e. whether it is a working
day or weekend/holiday), relatively precise hourly and possibly even inter-hourly
profiles can be generated.
The presence of smart measuring devices in the form of smart plugs and
cables which report real-time consumption per appliance in a home, or their
substitution with an NILM algorithm as described in Subsect. 3.1 where bad
performance due to insufficient generalization is not an issue, provides the pos-
sibility of predicting demand on a per-appliance level. This approach is scarcely
depicted in contemporary research articles with only a few papers like [28,312]
and [226] exploring this subject. Alternatively, the problem of demand fore-
casting is most often approached from an aggregated perspective, through the
prediction of neighbourhood, city or state-level consumption, with data avail-
ability generally being the driving factor that ultimately decides what type of
demand will be estimated. Time series from Figs. 4,5and 6illustrate the dif-
ferent dynamics of the demand signals from a single appliance, all appliances of
one home and several aggregated homes. Since each of these applications usu-
ally requires different levels of prediction precision, the raw data used for these
illustrations was averaged with different sample intervals (15 s, 60 s and 15 min)
in accordance with the appropriate use case.
08:38:00 09:07:00 09:36:00 10:05:00 10:34:00 11:02:00 11:31:00 12:00:00
0
1
2
time
power [kW]
Fig. 4. Typical washing machine demand profile with 15 s averages (showing what
appear to be two activations in the span of 4h)
Chapter 10 Case Study from the Energy Domain 177
00:00 04:48 09:36 14:24 19:12 00:00
0
1
2
time
power [kW]
Fig. 5. Total household demand profile with 60s averages (showing several appliance
activations during a full 24-h period)
00:00 04:48 09:36 14:24 19:12 00:00 04:48 09:36 14:24 19:12 00:00
0
5
10
15
time
power [kW]
Fig. 6. Aggregate household demand profile with 15 min averages (showing consump-
tion for 2 days with time-of-use tariff)
4.2 Production Forecaster
It has already been mentioned that energy planning is crucial for grid stabil-
ity, and that it highly depends on the forecast renewable energy sources (RES)
production. Therefore, in this subsection different methodologies used for pro-
duction forecasting are going to be covered as well as their relation to the field
of big data.
The production of RES technologies is highly influenced by weather con-
ditions. For example, there is very high dependency between PV production
and solar radiation, similar to the relationship between wind turbines and wind
speed and direction. In Table 1, the selection of weather services is given followed
by their main characteristics. Namely, depending on the practical application,
production forecasters can have different time resolutions and horizons, which
dictates the necessary weather forecast parameters. Therefore, from the above-
mentioned table, it can be seen that Darksky can provide estimations in terms of
minutes, whilst its horizon, as some kind of compromise, is only 7 days. Addition-
ally, depending on the approach, historical weather data might be necessary for
the purpose of the training process, as, currently, the most popular approaches
178 D. Puji´cetal.
in the field of RES production are data-driven algorithms. Finally, the choice
of weather service highly influences its price. All of those characteristics can be
found in the table.
Table 1. Overview of forecasting data providers
Name Min.
forecast
resolu-
tion
Max.
horizon
[days]
Historical
data
Fre e
up to
Coverage
OpenWeatherMap hourly 30 Yes 60 calls/minute Global
Wea t h e r b it hourly 16 Yes 500 calls/day Global
AccuWeather hourly 15 prev. 24 h 50 calls/day Global
Darksky minute 7Yes 1000 calls/day Global
weathersteak hourly 14 Yes 1000 calls/month
Yahoo! Weather hourly 10 No 2000 calls/day Global
The Weather Channel 15 min 30 Yes Global
World Weather Online hourly 15 Yes Not free Global
Depending on the practical application apart from input weather parameters
developed methodology varies, as well. For the use cases in which few mea-
surements are available, physical models are usually chosen. These models are
based on mathematical models and are usually deployed when there are not
enough real world measurements. These models are characterized with the low-
est performances in comparison with the following ones, but exist in cases of
missing data. This methodology is present in the literature for various RES such
as photo-voltaic panels (PVs) [115,334], wind turbines (WTs) [273] and solar-
thermal collectors (STCs) [80,394]. However, even though they do not require
huge amounts of measurements, physical characteristics such as number of solar
panels, position of panels and wind turbines, capacity etc. are needed and some-
times, again, inaccessible. Taking into account suppliers’ tendency to equip the
grid with numerous IoT sensors nowadays, the necessity of physical models is
decreasing, leaving room for data-driven models, which are a more important
part of this chapter and within the field of big data.
Currently the most popular and explored topic in the field of RES production
forecasters is statistical and machine learning (ML) based techniques, which
were proven to achieve higher performances but require substantial amounts of
data. Nonetheless, bearing in mind that a huge amount of big data is currently
available in the energy domain, these approaches are not common only amongst
researchers but also in real practice. The first group that stands out are the
statistical autoregressive methodologies SARIMA, NARIMA, ARMA, etc. [437].
They are followed by probabilistic approaches, such as in [452]. Finally, neural
networks and machine learning-based approaches are proven as one of the most
suitable choices [205,236,453], similar to numerous other fields.
Chapter 10 Case Study from the Energy Domain 179
Apart from the similar inputs regarding weather parameters and applied
models for RES production forecasters, all of the methodologies are dependent on
the estimation time horizon. Depending on the practical application, the orders
of magnitude can range from minutes to years. Further post-processing of the
obtained forecast results is another important factor. Apart from the grid control
and stability, from the perspective of big data the analytical tool developed
on top of the results provided by the forecaster could be exploited for failure
and irregularity detection in the system together with its high level metadata.
By contrast, outputs with the big time horizon could be seen as adequate for
extracting conclusions on a yearly basis using big data tools already presented
in this book.
4.3 Pricing Prediction
Another important application of prediction algorithms in the energy domain
are price predictions. As energy sectors worldwide are becoming increasingly
deregulated, variable pricing in energy trading is becoming increasingly promi-
nent with some envisioning a not-so-distant future where the cost of energy
in the wholesale and maybe even retail markets will be changing every 15 min
while the standard nowadays is usually hourly changes at most. Having accurate
predictions of wholesale market prices presents key information for large-scale
energy traders because it provides an insight into future trends in the same way
as stock price predictions do and allows for sound investment planning.
Wholesale price variations greatly impact retail prices, which, in turn, have a
key influence on the shape of the expected demand curve from end users. Moving
from fixed pricing to first time-of-use tariffs and later hourly variable pricing has
allowed for energy retailers to have granular control of load levels through what
is essentially implicit demand response (DR) where load increase or decrease
events are defined by the current prices. Energy prices are also influenced by the
availability of renewable sources. For example, systems with high PV penetration
tend to have lower prices during mid-day production peaks to try and motivate
users to consume more energy when there is a surplus in the system. In that
way, demand predictions, production predictions and pricing productions are
mutually interconnected in such a way that should result in a balanced system
of equal supply and demand.
5 Conclusion
The brief overview laid out in this chapter provides an insight into some poten-
tial applications of big data-oriented tools and analytical technologies in the
energy domain. With the importance of climate change mitigation growing by
the day, the number of solutions working towards increasing energy efficiency and
responsible energy use is only expected to rise. As such, this domain provides
an interesting and challenging realm for novel research approaches.
180 D. Puji´cetal.
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... Taking this into account, further research is needed to achieve adoption of the above mentioned novel concepts and technologies in the existing proprietary solutions that are deployed in the Serbian energy value chain for optimal energy production, distribution and consumption. [11] Recurrent Neural Networks (RNNs) with the accent on Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs) Wind turbine production forecaster [12] Deep neural networks; various architectures have been tested including different combinations of Long-Short Term Memory (LSTM), Convolutional, Dense and Dropout layers. Consumption prediction [13] Random forests, K-Nearest Neighbor, Support Vector Machines, Linear regression and Neural networks. ...
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