A Data-Driven Methodology for Heating Optimization in

Smart Buildings

Victoria Moreno

, Jos

e Antonio Ferrer

, Jos

e Alberto D

ıaz

, Domingo Bravo

and Victor Chang

Department of Energy, Research Institute of Energy and Environment of Heidelberg (ifeu), Heidelberg, Germany

Department of Energy, Energy Efﬁciency in Buildings Unit, CIEMAT, Madrid, Spain

Xi’an Jiaotong Liverpool University, Jiangsu, China

Keywords:

Big Data, Data Modeling, Smart Buildings, Energy Consumption, Optimization.

Abstract:

In the paradigm of Internet of Things new applications that leverage ubiquitous connectivity enable - together

with Big Data Analytics - the emergence of Smart City initiatives. This paper proposes to build a closed loop

data modeling methodology in order to optimize energy consumption in a fundamental smart city scenario:

smart buildings. This methodology is based on the fusion of information about relevant parameters affecting

energy consumption in buildings, and the application of recommended big data techniques in order to improve

knowledge acquisition for better decision making and ensure energy efﬁciency. Experiments carried out in

different buildings demonstrate the suitability of the proposed methodology.

1 INTRODUCTION

Recent advances in Internet of Things (IoT) technolo-

gies (Wortmann et al., 2015) have led to ever increa-

sing deployments of sensors, computing infrastruc-

tures and data proliferation in all aspects of daily life.

This opens opportunities to analyze and increase the

efﬁciency of existing solutions as well as to provide

completely new and innovative services in modern

cities. At global scale, cities represent three quar-

ters of the energy consumption and contribute 80%

of CO2 emissions (Provoost, 2013). In this context,

buildings are major consumers of energy that pro-

duce signiﬁcant amounts of Green House Gas (GHG)

emissions. Therefore, improving energy efﬁciency in

buildings is a main target so as to address resource

scarcity and realize international climate preservation

goals.

During last years several analysis about energy

efﬁciency in buildings have been carried out (Agar-

wal et al., 2010). Nevertheless, most of the proposals

to date only provide partial solutions to the problem

(Foucquier et al., 2013). The high volume of data

that can be generated by smart cities provides a great

scenario to implement Intelligent Building Manage-

ment Systems (IBMS). In this sense, Big Data Ana-

lytics (Iqbal et al., 2016) helps us to leverage the huge

amounts of data provided by IoT-based ecosystems to

reveal insights that help extract knowledge from them.

In this paper we propose a methodology to op-

timize the energy consumption of buildings through

IoT technologies and the application of big data tech-

niques. With the goal of providing anticipated respon-

ses to ensure energy efﬁciency in buildings, we iden-

tify the main drivers of energy use in building heating

systems - which implies the major energy consump-

tion in buildings - in order to model their impact using

all the information provided by sensors installed in

buildings and in their surrounds. These models are

used later to design optimal control strategies to save

energy. In order to carry out these steps, we propose

a general data-driven identiﬁcation methodology to

build a closed loop data modeling system for energy

consumption optimization in buildings. Thus, focu-

sing on the heating systems of buildings, we apply our

proposed methodology to deﬁne optimal strategies to

achieve minimum energy use, subject to speciﬁc in-

door comfort targets. Finally, we verify our metho-

dological approach through different experiments in

daily heating system operation of several reference

buildings. Hence, the contributions of this paper are

as follows:

• Design of a methodology to build closed loop

data modeling systems for buildings in order to

Moreno, V., Ferrer, J., Díaz, J., Bravo, D. and Chang, V.

A Data-Driven Methodology for Heating Optimization in Smart Buildings.

DOI: 10.5220/0006231200190029

In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), pages 19-29

ISBN: 978-989-758-245-5

optimize their energy consumption through IoT

technologies and the application of big data tech-

niques.

• Application of such methodology in different

buildings to get energy savings considering data

coming from different sources.

The remainder of this paper is structured as fo-

llows: Section 2 identiﬁes main parameters affecting

energy consumption of heating systems in buildings

and describes the main foundations of our methodo-

logy. Section 3 describes the different applications of

the proposed methodology pursuing the energy con-

sumption optimization acting over the building heat-

ing operation. Section 4 analyzes the main results ob-

tained after experimentation. Finally, Section 5 gives

some conclusions and an outlook of future work.

2 DATA MODELING FOR

ENERGY EFFICIENT

BUILDINGS

2.1 Parameters Affecting Energy

Consumption and Thermal Comfort

in Buildings

Different energy consumption proﬁles are associated

to buildings with different functionalities and proper-

ties. Therefore, when selecting sensors to be installed

in a building with energy efﬁciency aims, previously

it is necessary to carry out an initial characteriza-

tion of the main contributors to the building energy

use. At this regard, there are some mathematical mo-

dels which describe the thermal comfort responses of

buildings considering the impact of different factors,

for instance the models given by ASHRAE (Berglund,

1977). After analyzing these models, we describe in

this section the main parameters identiﬁed as relevant

due to their impact in the energy consumption and

thermal conditions of buildings. So, all these para-

meters should be monitored, analyzed and modeled

before implementing any optimum heating strategy to

save energy. The identiﬁed parameters as relevant are

the following:

• Environmental Conditions. The energy con-

sumption of buildings associated to heating sys-

tems is directly related to parameters such as tem-

perature, humidity, wind speed and solar radia-

tion.

• Occupants’ Behaviour. Occupants’ behaviour

is a relevant parameter which affects energy con-

sumption of buildings. In order to quantify its im-

pact, ﬁrstly it is necessary to solve the indoor lo-

calization problem and try to infer the occupants’

activity level.

• Information about Energy Consumption. Ha-

ving the real value of the energy consumed every

day even every hour lets users acquire know-

ledge about the impact of their performance on

the energy waste. Furthermore, this information

is useful in order to identify and adjust any devia-

tion between the predicted energy consumption

and the measured value.

• Information about Energy Generated. In such

cases where there are alternative energy sources,

knowing the value of the energy generated any-

time can be used to balance the energy consump-

tion of the building. Therefore, values about

the energy generated associated to the speciﬁc

contextual features can be used to model the

energy generation. This lets us to design opti-

mal energy distribution and usage strategies to get

more energy-efﬁcient buildings.

Once we have data about these parameters, it is

possible to implement a data-driven model identiﬁca-

tion to address the model representation of the heating

system to be controlled.

2.2 Data Model Identiﬁcation

There are several proposals in literature regarding to

data model identiﬁcation. Hence we can ﬁnd the so-

lution proposed by SEMMA, an acronym for Sample,

Explore, Modify, Model, Assess used by USA Insti-

tute Inc., or CRISP-DM, an acronym for CRoss Indus-

try Standard Process for Data Mining as deﬁned by

the CRISP-DM consortium (Wirth and Hipp, 2000),

and also there is the solution based on the KDD-

process (Liu and Motoda, 2012). The proposal given

by CRISP-DM has been developed by a consortium

of large companies (such as NCR, Daimler and SPSS)

and appears to be the most widely used process model

for intelligent data analysis today. It consists of six

phases explained in (Berthold et al., 2010).

Inspired by CRISP-DM, we formulate our own

methodology to build a closed loop data modeling

system reusing the already installed building infras-

tructure, turning legacy buildings into smart build-

ings. This methodology applies big data techniques to

steer building operation towards higher levels of efﬁ-

ciency in daily operation. In summary, the proposed

methodology consists of the steps showed in Figure

Big data techniques are applied mainly in the data

modeling step of this methodology. Big data tech-

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

Offline phase

Data management

Data modeling

Simulation and validation

What data do we have available?

Is the data relevant to the problem?

Is the data quality, quantity, recency sufficient?

Which data should we concentrate on?

How is the data best transformed for modeling?

What is the best technique/method

to get the model?

How well does the model perform

technically?

Predict and optimize

Actuate

Based on the generated models and the real-

time data

Control of the building infrastructure

Continuous adaptation of the predicted

models based on prediction errors (optional)

Adaptation

Prediction error

Deviation

model

Online phase

Energy savings

Figure 1: Steps of the data-driven methodology proposed for energy consumption optimization.

niques can be classiﬁed into three categories accor-

ding to their goals: descriptive, predictive and pres-

criptive (LaValle et al., 2011). In our methodology we

implement the three categories as described in next

subsections.

2.2.1 Application of Descriptive Big Data

Analysis

For such sensor readings with event-based nature it

is needed to carry out the data processing in a timely

manner. This task is undertaken following descrip-

tive big data techniques. In our case, we are going

to apply Complex Event Processing (CEP) approa-

ches (Cugola and Margara, 2012) in order to follow

a condition-action paradigm able to ﬁlter, correlate

or aggregate several streams of events. Unlike other

rule-based approaches, the key feature of the CEP

approach is that it is specially designed to operate in

nearly real time.

2.2.2 Application of Predictive Big Data

Analysis

For such sensors reporting data with a predetermined

frequency it is possible to generate predictive mo-

dels representing the behaviour patterns of the sensed

phenomena through the application of predictive big

data techniques. In this case, ﬁrstly, it is necessary

to carry out the identiﬁcation of the inﬂuential pa-

rameters by exploratory data analysis (for instance,

cross-correlation analysis). Once selected the relevant

parameters affecting energy consumption and indoor

comfort, it is time to transform relevant data into in-

put feature vectors of the models. In this case, when

studying the different aspects affecting heating energy

consumption and indoor temperature trends in build-

ings, we follow a general process to generate the pre-

dictive models which can be used to design optimiza-

tion strategies for the heating operation. The predic-

tive data modeling process followed is summarized

below:

1. Partitioning of input feature vectors into train-

ing data set (75%) and test data set (25%) (since

these proportions respond very well to our mode-

ling problem and because they are the proportions

usually applied in literature).

2. Normalization of each input feature of the training

data set to be centered around the origin with a

standard deviation of 1.

3. Investigation whether the application of Princi-

pal Components Analysis (PCA) to the input fea-

tures and retaining principal components accoun-

ting for 90% of feature variation improves model

accuracy.

4. Model training with 10-fold cross validation and

5 repetitions.

All these steps can be covered using different ana-

lytic tools: R, python, Matlab, etc. In our case we use

the open source statistical software R. From related

work in the building energy domain, we identify se-

veral regression techniques as applicable to the stud-

ied context. Applying the mentioned predictive mo-

deling process, we evaluate each technique in terms

of their predictive performance. Then, the best per-

forming models will serve later as input of the heating

optimization phase. To ﬁnd the optimal conﬁguration

A Data-Driven Methodology for Heating Optimization in Smart Buildings

Figure 2: Example of strategy to predict energy consumption of buildings based on weather forecast and occupancy schedule.

of the tuning parameters of each one of the evalua-

ted techniques, we use the R Package caret (Kuhn,

2008). So, to achieve the best model performance on

the test set we apply: (i) a grid search on the method’s

tuning parameters; (ii) different possible combination

of BMS data as input feature vectors; and, (iii) the bi-

nary choice whether to use PCA based input feature

transformation or not. Taking into account the domain

speciﬁc metrics to assess each model’s performance,

we use the following performance indicators:

• The test data performance is evaluated with the es-

tablished Root Mean Squared Error (RMSE) (see

Eq. (1)) and R-Squared (R2) metrics.

• After a test for normality of the residuals, the

RMSE’s standard deviation (SD) provides infor-

mation on model stability.

• To understand the magnitude of the RMSE of each

model, we examine the RMSE in relation to the

observed mean of the regressed variable (denoted

as CVRMSE, see Eq. (2)).

RMSE =

∑

i=1

− ˆy

)

(1)

where ˆy

is the value predicted by the model and

is the value actually observed.

CV RMSE =

RMSE

¯y

(2)

where ¯y is the mean of the values observed.

Figure 2 shows a schema of the predictive data

modeling process applied to generate energy con-

sumption models, considering as inputs the building

environmental conditions and the occupancy pattern.

2.2.3 Application of Prescriptive Big Data

Analysis

A kind of optimization solution is proposed in this

work applying a prescriptive approach for the data

modeling step based on Genetic Algorithms (GAs).

We use the GA implementation provided by R in the

genalg package (Willighagen, 2005). Focusing on the

optimization of the heating operation for example, we

try to keep thermal comfort conditions at the same

time that energy consumption restrictions are consi-

dered.

3 CASE STUDIES

Due to the fact that it is possible to ﬁnd buildings with

different sensed data available (because their techno-

logical infrastructure may be different), we decide to

tackle independently the impact of each parameter

identiﬁed in Section 2 in building energy consump-

tion. It lets readers to decide how many parameters

they want to consider when implementing different

heating optimization strategies. This way, in this sec-

tion we explain the four case studies which address

the implementation of different heating optimization

strategies based on: (i) the prediction of environmen-

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

tal conditions; (ii) the prediction of occupants’ be-

haviour; (iii) the provision of feedback to occupants

about energy consumption; and, (iv) the integration of

information about the energy generated by renewable

sources.

The three ﬁrst case studies have been carried out in

the Technological Transfer Centre (TTC) of the Uni-

versity of Murcia (ttc, 2016). In this building there are

a lot of sensors, controllers and actuators deployed

and integrated in an automation system which co-

llects data and executes control actions with the aim

of improving the indoor comfort at the same time that

energy efﬁciency is ensured. The fourth and last case

study has been carried out in ﬁve different buildings

with similar features and located in ﬁve geographical

locations of Spain (the University and Solar Platform

of Almeria, Madrid, Soria and Asturias) with diffe-

rent climate conditions. These buildings are being

used for research purposes in the frame of the SSP-

ARFRISOL project (arf, 2016).

3.1 Case Study 1

For this ﬁrst case study we have, on the one hand,

historical observations of temperature and humidity

collected in a weather station installed in the roof of

the TCC. This allows us to use univariate time se-

ries analysis, where the endogenous variable is go-

ing to be explained by its own antique performance.

When predicting outdoor environmental conditions,

we can consider time series models such as ARIMA

(Auto-Regressive Integrated Moving Average). They

have been widely used in order to predict tempera-

ture (Hippert et al., 2000), humidity (Shamsnia et al.,

2011), solar radiation (Hejase and Assi, 2012), wind

speed (Palomares-Salas et al., 2009), etc. In ARIMA

models the output is expressed as a function of past

values or lags (autoregressive part, see Eq. (3)) and

past errors or residuals (moving average part, see Eq.

(4)).

= µ +

∑

i=1

t−i

+ ε

, (3)

where µ is a constant, λ

is the coefﬁcient for the

lagged variable in time t − p and ε

is an error term.

= µ +

∑

i=1

t−i

+ ε

, (4)

The R package forecast (Hyndman and Khan-

dakar, 2008) permits to use an automation search of

the ARIMA parameters with the function auto.arima

(AA).

On the other hand, we have been also collec-

ting forecast data every hour with an horizon of 36

hours from external open data sources like Weather

Underground (und, 2016). This allows us to feed

the algorithm not only with past observations, but

also using this forecast variable as regressor. So,

we are going to consider also the AA with Regre-

ssors (AAR) and the programmed ARIMA by hand

(PA) with Regressors (PAR). Using the environmental

forecast, we can also apply different regressive tech-

niques implemented in R having as input the fore-

cast and as output the real environmental measure-

ments. Among others, we decide to evaluate the fo-

llowing techniques: Multi Layer Perceptron (MLP)

(Kalogirou, 2000), Bayesian Regularized Neural Net-

work (BRNN) (Hawarah et al., 2010), Support Vec-

tor Machines (SVM) (Fu et al., 2015), Gaussian Pro-

cesses with Radial Basis Function Kernel (GAUSS)

(Leith et al., 2004) and Random Forest (RF) (Zhao

and Magoul

es, 2012). Finally, it is also proposed the

combination of such techniques with the ARIMA pro-

cess described before.

We are going to base the pre-processing step

on the application of the Box-Cox transformation

(Robert H. Shumway, 2010), which stabilizes the

variance of the time series and also approximates it

to a normal distribution. In order to asses the model’s

stability over time we carry out a window rolling ana-

lysis of the performance. Traditionally, the rolling

window has had a ﬁxed size through the sample but

we are sticking to the reality of the application when

considering all the previous observations to be part of

the train set, as can be observed in Figure 3, where for

each step the green block is the test set and the orange

block is the train set.

Figure 3: Rolling window schema.

When using the mentioned regressive techniques,

and following all the steps of the big data predic-

tive approach of our methodology (see Figure 2),

the best hyper parameters are selected and we pre-

dict the next horizon using the model, as we would

do in a real-time situation. The models are evalua-

ted using the rolling windows strategy for 50 win-

dows being the selected horizon of 24 hours. We

tried different ARIMAS: (1) Automatic without re-

gressors (AA); (2) 3 ARIMAs conﬁgured by hand

A Data-Driven Methodology for Heating Optimization in Smart Buildings

15 20 25 30

Confidence intervals errors temperature

CVRMSE

●

BRNN_AR

BRNN_A

MLP_A

MLP_AR

MLP

BRNN

GAUSS

GAUSS_AR

GAUSS_A

RF_A

RF_AR

SVM_A

SVM_AR

SVM

A1R

A2R

A3R

AAR

●

● ●

●

5 10 15 20

8 10 12 14 16

24 h prediction vs real temperature

hour

temperature

●

real

predicted

Figure 4: Conﬁdence intervals CVRMSE temperature (left) and 24 hours predicted temperature with BRNN

vs real tempe-

rature (right).

without regressors (A1, A2, A3); and, (3) All with re-

gressors (AAR, A1R, A2R, A3R), having that the be-

tter performers are A1 and A1R. Then, for the com-

bination of these methods with the rest of machine

learning techniques, we have used the predictions

given by A1 and A1R. Consequently, we are go-

ing to consider 23 inputs candidates to use as in-

puts in this kind of model: external predictions

(BRNN, MLP, RF, GAUSS, SV M), univariate ARIMA

predictions (A1, A2, A3, AA), ARIMA with regres-

sor predictions (A1R, A2R, A3R, AAR), and the follo-

wing combined techniques: (BRNN

, MLP

, RF

GAUSS

, SVM

) or without regressors (BRNN

MLP

, RF

, GAUSS

, SV M

In Figure 4 (left) we appreciate the conﬁdence in-

tervals of the errors (CVRMSE) having that the best

one is BRNN combined with AR (BRNN

). It re-

turns a percentage of error with mean 15.79%, and

lower and upper conﬁdence intervals of 14.25% and

17.22%, respectively. Anyway, it is appreciable that

differences between the errors of the ﬁrst ﬁve models

are almost indiscernible. Also, Figure 4 (right) shows

one day’s prediction using BRNN (Hawarah et al.,

2010) combined with AR compared with the real ob-

servations for temperature. Doing the same process

for humidity we have reached a CVRMSE of 17.13%,

and lower and upper conﬁdence intervals of 14.6%

and 19.67%, respectively, when using also BRNN

combined with AR predictions (BRNN

). These re-

sults are closely followed by MLP (Kalogirou, 2000)

with AR, MLP and BRNN. So, both for predicting

temperature and humidity in our target building, we

are going to use the combination of BRNN and AR.

Once we are able to predict outdoor temperature

and humidity, we are going to use such predictions to

infer the building energy consumption associated to

them. For this, we apply some of the same regressive

techniques as used before: BRNN, MLP, RF, GAUSS

and SVM. The reason to use the same techniques is

because they have been already proposed in literature

for this objective (Neto and Fiorelli, 2008). The best

results were obtained when using BRNN with 15 neu-

rons, obtaining an RMSE of 43.76 kWh, which only

represents the 10.29% of CVRMSE, and with the high

coefﬁcient of determination of 0.89.

For the energy consumption optimization we im-

plement a prescriptive approach for the data mode-

ling step based on GA. To evaluate our GA-based op-

timization strategy in terms of energy savings, con-

trolled experiments were carried out in the TTC build-

ing during ﬁve consecutive weeks between June and

February of 2015. The results show that we can

accomplish energy savings between 10% and 22%.

3.2 Case Study 2

Our solution to the indoor localization problem is

based on a technological combination composed by

an active RFID system and some IR transmitters

(see our previous work published in (Moreno et al.,

2016)). The RFID tags of our solution are IR-enabled

tags which include an IR sensor which is powered by

an IR transmitter placed on the inside walls of the

building. The reference tags communicate with an

RFID reader covering each of the target localization

areas. Our localization mechanism uses the RSSI va-

lues corresponding to the reference RFID tags placed

on the ceiling. Both the IR identiﬁed and the RSSI

information collected in the RFID reader is used to

estimate the localization of the occupants wearing a

monitored RFID tag.

In order to select the best regression technique to

solve our localization prediction problem, we have

made a comparative between different regression

techniques once they have been applied to our pro-

blem (following the same predictive modeling pro-

cess as in the 1st case study). After comparing the

results obtained from k-Nearest Neighbors (KNN),

MLP, Extreme Learning Machine (ELM) and Radial

Basis Functions (RBF) (these techniques are already

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

proposed in literature to solve this kind of problem),

the RBF was the technique that most accurate results

provided. So, we propose to use the RBF technique

for carrying out the user location estimation. Hence,

the RSSI tag value p

associated to the monitoring

RFID tag is provided as input to all functions of our

RBF estimator, and the output f (p

) is given by:

f (p

) =

∑

i=1

· ϕ(k p

− c

k) (5)

where k p

− c

k is the Euclidean distance bet-

ween p

and the RBF function with center c

. The

number of RBFs is C, and w

are the weights of the

network.

Hence, for each area covered by an IR transmi-

tter we have an RBF network implemented in order

to estimate the occupant position. Furthermore, with

the aim of ensuring that such position is possible ta-

king into account the previous positions of the target,

we implement for each RBF network a tracking me-

chanism which is used to ﬁlter each one of the esti-

mated positions. The tracking algorithm used to carry

out this stage of the proposed localization solution is

based on Particle Filters (PFs). Furthermore, every T

seconds the localization mechanism evaluates if there

are new measurements from the monitoring tag to es-

timate the next occupant’s location using the RBF net-

work already implemented. If there is updated in-

formation, the RBF network estimates the next target

position, but if it is not the case, the PF associated

to such RBF is applied to estimate the next position

based on the prior state of the target. Tracking pro-

cesses through PFs is the third and last step of our

localization mechanism.

Table 1: Accuracy results for different RFID reference tag

distributions.

Tag distribution Size (number of tags) RMSE (m.) SD (m.)

1m x 1m 1200 0.9 2.6

1m x 1.5m 1200 1.8 1.6

1m x 2m 1200 1.2 1

1.5m x 1.5m 1200 1.3 1.1

2m x 2m 1200 1.6 1.4

2m x 2.5m 1200 1.9 1.7

Taking into consideration different RFID refe-

rence tag distributions, the results obtained from the

tests performed in our test lab are represented as sta-

tistical values of the error achieved in the estimated

locations. Table 1 shows these results. As can be

seen, they are quite accurate according to the location

requirements imposed by indoor services, even using

a low number of reference tags.

After the identiﬁcation and localization of occu-

pants inside the building, different proﬁles of ther-

mal comfort for each occupant are generated using

the default settings according to their preferences.

Figure 5: Percentage of mean daily energy consumption sa-

vings in heating considering occupants’ behaviour.

In this way, considering accurate localization infor-

mation and the occupant’s comfort preferences for

the heating management process, energy wastage de-

rived from overestimated or inappropriate settings are

avoided. However, when occupants do not feel com-

fortable they can change the provided thermal settings

according to their own preferences. For this, users are

able to communicate their preferences to the system

through the control panels of the home automation

system which are associated to their location. A des-

criptive data modeling is used to implement the op-

timization process able to update the corresponding

occupant proﬁles as long as these values are within

minimal thermal comfort levels (Berglund, 1977). On

the other hand, when several occupants are sharing

the same heating system, our control solution is able

to provide them with comfort conditions that satisfy

the greatest number of them, applying for this a GA-

based optimization strategy. After experimentation,

an average of 91% of success in the predictions of

the thermal comfort conditions for occupants was ob-

tained.

Thus, and ﬁnally, we apply rules over the whole

body of the available knowledge related to the occu-

pants’ localization and their comfort preferences to

make decisions related with the control of the auto-

mated heating systems. In this case, following a des-

criptive approach for the optimization phase (based

on CEP rules). For evaluating the energy savings we

could get following this approach, we carried out a

comparison between two consecutive months in the

winter of 2013: January, without any energy manage-

ment, and February, with our intelligent heating ma-

nagement system running. We compared the energy

consumption value for each day of February with the

consumption associated to the same day of the previ-

ous month. Because such year February was only of

28 days, we included in the comparison the ﬁrst three

days of March to make a complete contrast for the 31

A Data-Driven Methodology for Heating Optimization in Smart Buildings

days of January. The energy saving obtained varied

between 14% and 25% (see Figure 5). Therefore, we

can state that the experimental results obtained reﬂect

clear energy savings.

3.3 Case Study 3

Energy monitoring technologies can help us to re-

duce energy consumption in buildings around 5% to

15% (Darby, 2006). These technologies are able to

provide real-time feedback on domestic energy con-

sumption. In this regard, there are studies which

state that providing feedback about energy consump-

tion to the occupants is one of the most successful

approach to let them acquire more knowledge about

the energy consumption proﬁle of their buildings and

save energy (Fischer, 2008). In this way, occu-

pants can involve themselves with the goal of mak-

ing a more responsible use of the energy. Following

this approach, occupants can become into system co-

designers and ﬁnal deciders of the control rules and

strategies implemented to save energy.

In our heating management system we provide

occupants with feedback about the hourly energy con-

sumption of the building and we consider the data

provided directly by them through their interactions

with the heating system when they change the com-

fort conditions provided to them automatically. Con-

sequently, the system learns and auto-adjusts accor-

ding to such changes applying for this a descriptive

approach for the data modeling of the optimization

strategy based on CEP rules. In order to evaluate

the energy saving impact of providing a user-centric

heating service in buildings, we carried out an experi-

ment during two months. During the ﬁrst 31 days of

the experiment, occupants lacked any feedback about

the energy consumption as well as any control capa-

bility over the setting of the heating systems. After

this, during the last 31 days of the experiment, occu-

pants were empowered to participate. In this case,

they were asked to deﬁne their own rules for con-

trolling the heating operation. Furthermore, during

this second phase of the experiment, the building au-

tomation system was displaying real-time informa-

tion about the energy consumption in kW, cost of the

energy consumed according to the its price in the mar-

ket, energy usage history, etc. Comparing both situa-

tion, we were able to get extra energy savings of 9% at

building level when users were actively participating

with the energy building management system.

3.4 Case Study 4

The SSP-ARFRISOL is a singular strategic project on

bioclimatic architecture and solar cooling that tries to

demonstrate that this kind of architecture is suitable

to make buildings energy efﬁcient. For this purpose,

ﬁve symbolic public buildings of ofﬁces, both new

and rehabilitated, are being analyzed theoretically and

monitored in real conditions of use after having opti-

mized its architectural design and its facilities. The

research goal of this project is to achieve that these

buildings uses between 10% and 20% of the conven-

tional energy thanks to the use of renewable energies

combined with passive strategies from the architec-

tural design of the building. In the same way, it is

desired to have reduction of the CO

emissions and

increase of the comfort.

Each building has a control and monitoring sys-

tem with a huge number of sensors, electrical and

computational infrastructure installed. Control is cen-

tered on systems - particularly HVAC systems; there

are a lot of sensors installed in its circuits at the points

of production, exchange and consumption (tempe-

rature, water ﬂow, condition of pumps and valves,

power, etc.). The systems basically operate based on

a descriptive approach for the data modeling of the

optimization strategy, i.e. based on set points, de-

mand and timetable settings. Its management system

consists of controllers of the IQ3 family required to

perform the control of the different parts of the in-

stallation, and a central station as a system supervi-

sor which allows us to change schedules, temperature

set points, supervise historical data, states of different

machines, etc. It interacts through a SCADA. Figure

6 shows a screen shot in which we can see the mea-

surements taken in real time.

An important use of the control data is to make

energy analysis. Measurements allow us to estimate

a complete energy ﬂow of the system: how much

energy is produced with conventional or renewable

origin, how much is lost in transport or storage, how

much is consumed in each terminal point, etc. The

monitoring system is more focused on the evalua-

tion of parameters such as the electricity consump-

tion according to the use, temperatures inside diffe-

rent rooms, air quality, external meteorological con-

ditions, use of the building, additional measures of

systems, etc. The variables and measurement points

have been selected with the aim of having them as

representative as possible. For the most critical points

and variables, redundancies have been established,

which have facilitated subsequent veriﬁcation as well

to carry out researches on the subject. Global mon-

itoring is carried out on the buildings, and more ex-

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

Figure 6: Deployments and measurements taken in real time.

haustive sets of enclosures/rooms are considered as

representative of each building. In the end, there are

about 200 sensors installed in each building. The con-

trol and monitoring systems complement each other:

the control system allows real-time interaction, and

the monitoring system performs a more exhaustive

and accurate sampling.

Energy saving strategies are individually tailored

to each building based on its location, resources and

climate. Active strategies are linked with an adequate

management of the energy to optimize its efﬁciency.

Some example of the strategies used are the follo-

wing:

• Heating and renewable DHW obtained through

solar collectors and biomass boilers.

• Renewable cooling by the combination of the so-

lar thermal ﬁeld with absorption machines.

• Pre-cooling by radio-convective ﬁeld.

• Geothermal energy (energy exchange systems),

suppression of cooling tower.

• Support of conventional energy by high efﬁciency

gas boiler.

4 DISCUSSION

In this section we are going to review the main results

obtained for each one of the case studies described in

the previous section.

• Case Study 1. Regarding to the application of

predictive big data analysis to estimate outdoor

environmental conditions, we have obtained that,

after comparing the predictive results of different

techniques, BRNN combined with AR predictions

are able to estimate outdoor temperature and hu-

midity with a CVRMSE of 15.79% and 17.13%,

respectively. Which are very suitable results con-

sidering that we are predicting in a horizons of 24

hours. Then, using both predictions we train the

model able to estimate the energy consumption

associated to the heating system. After analyz-

ing the results obtained with different regressive

techniques, the best performance is provided by

the BRNN technique with 15 neurons, getting the

10.29% of error percentage.

Finally, using the outdoor temperature and humi-

dity predictive models and the estimation of the

energy consumption, we implement an optimiza-

tion strategy based on a GA which is in charge of

indicating the optimal conﬁguration of the heat-

ing system to ensure energy efﬁciency, at the same

time that thermal comfort restrictions are conside-

red. After carrying out some experiments apply-

ing such optimization strategy, we get mean daily

energy savings between 10% and 22%.

• Case Study 2. Regarding to the application of

predictive big data analysis to estimate indoor lo-

calization, we have obtained that, after comparing

the predictive results of different techniques, an

RBF network combined with PFs are able to esti-

mate occupants’ localization with a mean error of

0.9 m. and 1.9 m. considering a tag distribution

of 1m x 1m and 2m x 2.5m, respectively. Then,

applying a descriptive data analysis approach we

are able to estimate individual occupants’ comfort

preferences. But, for the cases when more than

an occupant are sharing a same heating system,

a GA-based optimization mechanism is executed

to infer the optimal comfort preference. After ex-

periments, we achieved a 91% of success in the

estimation of occupants’ comfort preferences.

A Data-Driven Methodology for Heating Optimization in Smart Buildings

Finally, using the indoor localization mechanism

and the prediction of occupants’ comfort prefe-

rences, we implement an optimization strategy

through CEP-based rules to control the heating

systems. After experiments running such opti-

mization strategy, we are able to get mean daily

energy savings between 14% and 25%.

• Case Study 3. Regarding to the approach of pro-

viding occupants with information about the real-

time energy consumption of the building, and then

let them conﬁgure their own control rules - which

are translated into CEP-based control rules - we

got an extra mean daily energy saving of 9% con-

sidering the actuation over the heating systems.

• Case Study 4. When alternative energy sources

are available in buildings, it is possible to imple-

ments control strategies for the heating systems

based on CEP-rules considering both bioclimat-

ics and built conditions. Then, after carrying out

several experiments in different buildings with di-

fferent features, we were able to get energy sa-

vings between 80% and 90%.

5 CONCLUSIONS AND FUTURE

WORK

In this paper, we analyze the main factors impacting

the energy consumption associated with provisioning

comfortable indoor temperatures in buildings. Af-

ter this, we formulate a methodology for data model

identiﬁcation, modeling and control applying diffe-

rent techniques of big data.

Four case studios are implemented in different

buildings. They intend to demonstrate that energy

savings can be achieved when the individual impact

of each parameter affecting the energy consumption

in buildings is considered for controlling the heating

system. Thus, we are able to simplify the model relat-

ing the indoor thermal comfort provisioning and the

associated energy consumption of buildings. Never-

theless, different control strategies based on different

parameters could be running at the same time increa-

sing the total energy savings at building level.

The ongoing work is focused on this last issue,

i.e. the design of control strategies including simul-

taneously all the parameters addressed in this paper

for affecting energy consumption of heating building

systems.

ACKNOWLEDGEMENTS

This work has been funded by the Science and Tech-

nology S

eneca-Agency Foundation of Murcia Re-

gion (Spain) by means of the “Talento Investigador

y su Empleabilidad“ Program, Postdoctoral Category

(Consejer

ıa de Educaci

on y Universidades) (grant

19782/PD/15).

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A Data-Driven Methodology for Heating Optimization in Smart Buildings