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Energy consumption and comfort gap in social housing in Madrid, through smart meters and surveys information

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El trabajo que se presenta explora las posibilidades para desarrollar un método de detección de situaciones de pobreza energética a partir de los datos de consumos energéticos registrados por los nuevos contadores inteligentes que están siendo instalados en sustitución de los tradicionales contadores de pasos. Para ello, resulta necesario contrastar los datos de los consumos realizados con la información obtenida a partir de encuestas realizadas a los habitantes. Se analiza la información declarada por los usuarios sobre sus hábitos energéticos, las fuentes de energía contratadas y las instalaciones térmicas de la vivienda. La comparación de la información recopilada pretende esclarecer si las necesidades energéticas de las viviendas están siendo satisfechas. La metodología que se concreta en este trabajo es una adaptación al contexto residencial español de promoción pública, a partir del procedimiento desarrollado y testado para 400 viviendas en Portugal por los investigadores Gouveia y Seixas (2016, 2018) de la Faculdade de Ciências e Tecnologia de la Universidade Nova de Lisboa (FCT-NOVA). Se analizan en este caso 19 unidades de vivienda social en edificios multifamiliares representativos de la edificación de la periferia de Madrid producida entre los años 1940-80. A partir de los resultados se estudian las posibilidades del método para tratar de detectar la brecha entre las necesidades de energía (relacionadas con la satisfacción del confort térmico necesario para lograr unas condiciones de vida saludables) y el consumo que realmente se está dando en estas viviendas.
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91
Consumo energético y brecha de confort en viviendas
sociales de Madrid, a través de información de
contadores inteligentes y encuestas
Energy consumption and comfort gap in social housing in
Madrid, through smart meters and surveys information
Fernando Martín-Consuegra1, João Pedro Gouveia2, Fernando de Frutos1, Carmen
Alonso1, Ignacio Oteiza1
ABSTRACT
El trabajo que se presenta explora las posibilidades para desarrollar un método de detección de situaciones
de pobreza energética a partir de los datos de consumos energéticos registrados por los nuevos contadores
inteligentes que están siendo instalados en sustitución de los tradicionales contadores de pasos. Para ello,
resulta necesario contrastar los datos de los consumos realizados con la información obtenida a partir de
encuestas realizadas a los habitantes. Se analiza la información declarada por los usuarios sobre sus hábitos
energéticos, las fuentes de energía contratadas y las instalaciones térmicas de la vivienda. La comparación
de la información recopilada pretende esclarecer si las necesidades energéticas de las viviendas están siendo
satisfechas.
La metodología que se concreta en este trabajo es una adaptación al contexto residencial español de pro-
moción pública, a partir del procedimiento desarrollado y testado para 400 viviendas en Portugal por los in-
vestigadores Gouveia y Seixas (2016, 2018) de la Faculdade de Ciências e Tecnologia de la Universidade Nova
de Lisboa (FCT-NOVA). Se analizan en este caso 19 unidades de vivienda social en edificios multifamiliares
representativos de la edificación de la periferia de Madrid producida entre los años 1940-80. A partir de los
resultados se estudian las posibilidades del método para tratar de detectar la brecha entre las necesidades de
energía (relacionadas con la satisfacción del confort térmico necesario para lograr unas condiciones de vida
saludables) y el consumo que realmente se está dando en estas viviendas.
Key Words: eficiencia energética, confort, vivienda social, contadores inteligentes, encuestas.
(1) Instituto Eduardo Torroja de ciencias de la construcción, Consejo Superior de Investigaciones Científicas. Madrid, +34 913020440,
martin-consuegra@ietcc.csic.es (2) CENSE – Center for Environmental and Sustainability Research, NOVA School of Science and
Technology, NOVA University Lisboa, Portugal.
92 Consumo energético y brecha de confort en viviendas sociales de Madrid, a través de información de contadores inteligentes y encuestas
1. Introducción
HABITAres, es un proyecto de investigación cen-
trado en la rehabilitación energética del parque de
edificios ineficientes ubicados en áreas de bajos in-
gresos de la ciudad de Madrid (España). Está siendo
desarrollado por el Grupo de Investigación “Siste-
mas Constructivos y Habitabilidad en la Edificación
(SCHE)” del Instituto Eduardo Torroja de Ciencias de
la Construcción. El objetivo del proyecto es generar
modelos energéticos del parque de edificios a escala
urbana, calibrados mediante la monitorización del
desempeño energético de una muestra de casos. Se
establece un marco estadístico y físico del edificio
para caracterizar y evaluar el rendimiento energé-
tico de los edificios en su estado actual basándose
en la medición de datos reales (Roels, 2019). El “Big
Data” recopilado por los medidores inteligentes es
una nueva fuente de información del consumo de
energía de los edificios. Esta metodología ha sido
desarrollada y probada para una muestra de 400 vi-
viendas en Portugal en un primer estudio en el que
se establecieron grupos de consumidores basados
en perfiles anuales y niveles de consumo (Gouveia
y Seixas, 2016). Los resultados llevaron a una inves-
tigación adicional, los perfiles de electricidad dia-
rios se utilizaron de una muestra más pequeña y se
combinaron con datos de temperatura (Gouveia et
al., 2017). En una etapa siguiente, dos grupos de con-
sumidores distintos fueron evaluados en mayor pro-
fundidad combinando la información de Medidores
inteligentes con modelos de simulación de energía
de edificios (Gouveia et al., 2018). Esta investigación
atrajo el interés del grupo SCHE, que actualmente
se encuentra incorporando datos reales a modelos
energéticos de la ciudad de Madrid. El trabajo que
aquí se presenta es la exploración de las posibilida-
des para utilizar la metodología desarrollada en Por-
tugal para una muestra de vivienda social en Madrid.
2. Objetivos
El objetivo de esta investigación es la exploración de
las posibilidades de utilizar información sobre regis-
tros de consumo de energía de los nuevos medido-
res inteligentes (que las empresas de energía están
instalando cada vez más en sustitución de los con-
tadores de pasos tradicionales), como información
relevante para la investigación energética de la edi-
ficación residencial En el Proyecto HABITAres está
siendo monitorizada una muestra de 19 viviendas
sociales en edificios multifamiliares en la periferia
de Madrid, siguiendo el procedimiento descrito en
(Alonso et al., 2017). Para definir las características
de la monitorización, se realizó una encuesta a los
usuarios de cada vivienda acerca de las fuentes de
energía utilizadas, los electrodomésticos, la autoper-
cepción del bienestar y los hábitos de los ocupantes.
El objetivo es la combinación de los datos de con-
sumo de energía de los medidores inteligentes con
la información obtenida de las encuestas para deter-
1. Introduction
HABITAres, is a research project focused on the ener-
gy refurbishment of the inefficient building stock
located in low income areas of the city of Madrid
(Spain). It is developed by the Research Group Siste-
mas Constructivos y Habitabilidad en la Edificación
(giSCHE) from Instituto Eduardo Torroja de ciencias
de la construcción. The aim of the project is to gene-
rate energy models of the building stock at an urban
scale, calibrated by monitoring the energy perfor-
mance of selected cases. A statistical and building
physical framework is set to characterize and assess
the actual energy performance of buildings based
on the measurement of real data (Roels, 2019). Big
data collected by smart meters is a growing sour-
ce of information of detailed energy consumption
in buildings. This information has been developed
and tested on a sample of 400 houses in Portugal by
Gouveia et al: on a first study, cluster groups of con-
sumers were set based on the annual profiles and
levels of consumption (Gouveia and Seixas, 2016).
The results led to additional research, daily electricity
profiles were used from a smaller sample and com-
bined with temperature data (Gouveia et al., 2017).
On a next stage, two distinct consumer groups were
assessed deeper combining Smart Meters info with
buildings energy simulation models (Gouveia et al.,
2018). This research attracted the interest of giSCHE
which is actualy incorporation real measured data to
urban energy models of the city of Madrid. The work
presented here is the exploration of the possibilities
for using the methodology developed in Portugal to
a sample of Madrid social housing.
2. Objetives
The aim of this research is to explore the possibi-
lities of using the information about energy con-
sumption records in the new smart meters (that are
increasingly being installed by energy companies
in substitution of the traditional step counters) as
reliable information for energy research of the resi-
dential building stock. A sample of 19 social housing
dwellings in multifamily buildings in the outskirts of
Madrid is being monitored by Proyecto HABITAres
following the procedure described in (Alonso et al.,
2017). Previously to the monitoring period, a survey
has been conducted to the users of each dwelling
to characterize energy sources, appliances, self-per-
ception of comfort and habits of the occupants.
The main objective is to combine energy consump-
tion data from smart meters with the information
obtained from door to door surveys in order to de-
terminate if it’s possible to detect energy poverty
situations based on these two datasets. External
temperature information is also included in order to
incorporate climatic conditions. Information about
the gap between energy needs and real consump-
tion, related to comfort, is to be also explored.
93
Fernando Martín-Consuegra
minar si es posible detectar situaciones de pobreza
energética y recopilar información sobre la brecha
entre las necesidades de energía y el consumo real.
3. Metodología
En primer lugar, se resume y analiza la información
de las encuestas realizadas a los ocupantes de las
viviendas. En un segundo paso se recopila la infor-
mación sobre el consumo de energía registrado en
varias unidades de vivienda por medidores inteli-
gentes, y se relaciona con la temperatura externa.
Este proceso se realiza para los usos energéticos
declarados por los ocupantes en las encuestas (elec-
tricidad y gas natural). Finalmente, la información se
compara analizando la cantidad de energía utilizada
por el hogar, relacionada con los hábitos declarados,
y se desarrollan conclusiones sobre la detección de
la pobreza energética a través de la información re-
gistrada por medidores inteligentes. La encuesta
dura aproximadamente 30 minutos y las preguntas
se centran en la vivienda, el hogar, la instalación y las
facturas.
Datos de vivienda. Superficie y antigüedad del
edificio, propiedad y preguntas generales sobre
deficiencias en construcción y tipo de ventanas.
Datos sobre el hogar. Número de habitantes, ocu-
pación media declarada, hábitos de ventilación y
hábitos de uso del equipo, comodidad declara-
da del hogar, medidas de mejora y medidas de
ahorro de energía que se tienen en cuenta en el
hogar.
Datos sobre la instalación de ACS, calefacción,
refrigeración y cocinas definidas por la fuente de
energía y el sistema de producción.
Datos de facturas eléctricas y gas. Se les pide una
factura de electricidad y gas según los diferentes
casos; El número incluido en la factura nos per-
mite acceder al consumo histórico. Son datos pri-
vados que obtenemos consultando al proveedor
siempre con el consentimiento del propietario de
la factura. El consumo se define en kWh con re-
gistro temporal variable.
Los datos climáticos se toman de AEMET OpenData
REST API (Interfaz de programación de aplicaciones,
Representación de transferencia de estado). Se des-
cargaron los datos mensuales promedio de tempe-
ratura exterior (ºC) entre los años 2015-2019 de la
estación meteorológica Retiro-Madrid. (http://www.
aemet.es)
4. Resultados
4.1. Información de encuestas
La respuesta de las encuestas relacionadas con el
uso de energía y el confort para las 19 viviendas se
3. Methodology
First, the information from surveys made to the oc-
cupants of the dwellings is summarized and analy-
zed. In a second step, information about the energy
consumption registered in several housing units by
smart meters, is collected and related to external
temperature. This process is done for the energy uses
declared by the occupants in the surveys (electricity
and natural gas). Finally, the information is compared
discussing the amount of energy used by household,
related to declared habits, and conclusions about
energy poverty detection through the information
registered by smart meters are developed. The sur-
vey lasts about 30 minutes and the issues are focu-
sed on housing, home, installation and bills.
Data on housing. System, surface and age of the
building. General questions about deficiencies in
construction windows.
Data about the home. Number of inhabitants,
declared average occupation, ventilation habits
and habits on the use of the equipment, declared
comfort of the home, measures for improvement
and on the energy saving measures taken into ac-
count in the home.
Data on the installation of DHW, heating, cooling
and kitchens defined by the energy source and
the production system.
Data of electric bills and gas. They are asked for an
electricity and gas bill according to the different
cases; the cups number included in the invoice
allows us to access historical consumption. They
are private data that we obtain by consulting the
supplier always with the consent of the owner of
the invoice. The consumption is defined in kWh
with variable temporal record.
Climatic data are taken from AEMET OpenData
REST API (Application Programming Interface, State
Transfer Representation). Average monthly outdoor
temperatura (ºC) data between the years 2015-2019
from Retiro-Madrid meteorological station were
downloaded (AEMET, n.d.).
4. Results
4.1. Information from surveys
The response of the surveys related to energy use
and comfort for the 19 dwellings are resumed in Ta-
ble 1.The information form each dwelling is anony-
mized by an ID number with two digits (the first one
corresponds to the building and the second to the
housing unit). Information about surface (surf), year
of construction (year), type of tenure (property or
rental), number of occupants and the number of the
floor of the building in which the dwelling is inclu-
ded. Also, declaration of users’ comfort in winter and
94
Tabla1.
Information from
surveys related to energy
consumption and comfort.
Consumo energético y brecha de confort en viviendas sociales de Madrid, a través de información de contadores inteligentes y encuestas
summer is collected, ventilation habits and the type
and energy source for space heating. The two last
columns represent the availability of smart meters in
electricity and natural gas.
From the information on the table it can be seen that
only 6 of the 19 dwellings have complete information
of the energy consumption of every source they use
(1.1, 1.2, 1.3, 3.2, 5.3 and 6.2). Cases 4.1, 4.2, 4.3 and 4.4
have information from smart meters but use district
heating so it is not possible to quantify their comple-
te consumption. Study cases with ID 2.1 and 2.2 are
single family housing.
6 of 19 housing units (32%) declared not to be
able to keep thermal comfort in their homes du-
ring summer and
8 of 19 housing units (42%) declared not to be
able to keep thermal comfort during winter.
Table 2 resumes the information about energy use
and production system for space heating, domestic
hot water (DHW) and space cooling. In Madrid, light-
ning and other electric equipment are always inclu-
ded on the electricity bill and are not easy to disa-
ggregate. Information about cooking energy needs
have been included because they can be provided
by different sources and means the highest energy
consumption on dwellings suffering from Energy
Poverty in Spain (Luxán García De Diego et al., 2017).
resume en la Tabla 1. La información de cada vi-
vienda se anonimiza mediante un número de iden-
tificación con dos dígitos (el primero corresponde
al edificio y el segundo a la unidad de vivienda). Se
incluye información sobre la superficie (surf), año de
construcción (year), tipo de tenencia (tenure), núme-
ro de ocupantes y el número del piso del edificio en
el que se encuentra la vivienda. Además, se recopila
la declaración de confort de los usuarios en invierno
y verano, los hábitos de ventilación y el tipo y la fuen-
te de energía para la calefacción. Las dos últimas co-
lumnas representan la disponibilidad de medidores
inteligentes en electricidad y gas natural.
Con la información de la tabla se puede deducir que
solo 6 de las 19 viviendas tienen información com-
pleta del consumo de energía de todas las fuentes
que utilizan (1.1, 1.2, 1.3, 3.2, 5.3 y 6.2). Los casos 4.1,
4.2, 4.3 y 4.4 tienen información de medidores inte-
ligentes, pero utilizan calefacción de distrito, por lo
que no es posible cuantificar su consumo total. Los
casos de estudio con ID 2.1 y 2.2 son viviendas uni-
familiares (unif).
6 de las 19 unidades de vivienda (32%) declararon
no poder mantener el confort térmico en sus ho-
gares durante el verano
8 de las 19 unidades de vivienda (42%) declararon
no poder mantener el confort térmico durante el
invierno.
ID surface year tenure occupancy floor Conf
sum
Conf
win
vent_
days
vent_
hours/
day
heating source SMelec SMgas
1.1 91 1965 prop 1 1-5 yes no 5 0.25 individual electricidad x
1.2 91 1965 prop 4 4-5 no yes 5 0.75 individual gas natural x x
1.3 93 1965 prop 2 4-5 yes yes 5 0.17 individual gas natural x x
2.1 82 1965 prop 2 unif yes no individual gas natural
2.2 75 1965 prop 3 unif yes no 5 0.5 individual gas natural
3.1 70 1965 prop 1 1-5 yes yes 7 0.5 individual gas natural x
3.2 70 1965 prop 4 3-5 yes yes 1 individual gas natural x x
3.3 70 1965 prop 1 5-5 yes yes 15 individual electricidad
4.1 73 1965 prop 3 1-6 yes yes district gas x
4.2 110 1965 prop 2 2-6 yes yes 5 0.08 district gasóleo x x
4.3 67 1965 prop 4 5-6 no no 5 0.5 district gasóleo x
4.4 117 1965 prop 4 6-6 yes yes 5 0.25 district gasóleo x
5.1 110 1982 prop 1 1-10 yes yes 5 21 individual gas natural
5.2 110 1982 prop 3 4-10 yes yes 5 0.33 individual gas natural x
5.3 110 1982 prop 3 9-10 no no 5 4 individual gas natural x x
5.4 110 1982 prop 5 10-10 no no 5 0.17 individual gas natural
6.1 70 1950 rent 2 1-2 yes yes 5 2 individual gasóleo x
6.2 74 1950 rent 4 1-2 no no 0.33 individual electricidad x
6.3 70 1950 rent 3 2-2 no no 1 individual electricidad
6.4 83 1950 rent 1 2-2 individual Gas natural x
95
Tabla2.
Energy source and
production system on the
dwellings of the sample.
Fernando Martín-Consuegra
Energy source Production system
Space heatingDHWSpace coolingcooking
La Tabla 2 resume la información sobre el uso de
energía y el sistema de producción para calefacción,
agua caliente sanitaria (ACS) y refrigeración. En Ma-
drid, la iluminación y otros equipos eléctricos siem-
pre están incluidos en la factura de la electricidad y
no son fáciles de desagregar. Se ha incluido informa-
ción sobre las necesidades energéticas de cocinas
porque pueden ser proporcionadas por diferentes
fuentes y significa el mayor consumo de energía en
viviendas que sufren la pobreza energética en Espa-
ña (Luxán García De Diego et al., 2017).
La calefacción supone el 55% del consumo energéti-
co en las zonas continentales de España (IDAE, 2011).
Heating corresponds to the 55% of energy consump-
tion in continental areas of Spain (IDAE, 2011). Main
energy source for heating in Madrid is natural gas
(it was 59% in 2001 Census), followed by gasoil and
electricity. Gasoil and natural gas are used for space
heating and DHW while electricity can be used for
every service. Statistical data from the 2011 census
has unfortunately lost precision in Spain comparing
to previous ones, so information about energy sour-
ces is no longer available (Naredo, 2014). Gasoil has a
high penetration in the sample, but this is not repre-
sentative of the use of energy on the building stock
in Madrid (INE, 2001). In this case it corresponds
mostly to a building heated by a district heating sys-
96 Consumo energético y brecha de confort en viviendas sociales de Madrid, a través de información de contadores inteligentes y encuestas
La principal fuente de energía para la calefacción en
Madrid es el gas natural (fue del 59% en el Censo de
2001), seguido del gasóleo y la electricidad. El gasó-
leo y el gas natural se utilizan para la calefacción y el
ACS, mientras que la electricidad se puede utilizar
para todos los servicios. Los datos estadísticos del
censo de 2011 lamentablemente han perdido preci-
sión en España en comparación con los anteriores,
por lo que ya no se dispone de información sobre
fuentes de energía (Naredo, 2014). El uso de gasoil
tiene una alta penetración, en la muestra, pero esto
no es representativo del uso de energía en el stock
de edificios en Madrid (INE, 2001). En este caso, co-
rresponde principalmente a un edificio servido por
un sistema de calefacción de distrito, que es inusual
en la ciudad. El butano se utiliza para ACS y para co-
cinas.
4.2. Información de contadores acerca del
consumo
El consumo de energía en la vivienda de Madrid se
basa principalmente en la electricidad y el gas natu-
ral. El gasoil y el butano no son fáciles de cuantificar
precisamente porque su oferta se basa en la deman-
da.
4.2.1. Consumo de electricidad
El suministro eléctrico en viviendas en Madrid tiene
casi un 100% de implantación (fuente). Todas las vi-
viendas de la muestra están conectadas a la red. La
información sobre el consumo de medidores inteli-
gentes está disponible para 12 unidades de vivienda
ubicadas en 6 edificios diferentes. La gráfica de los
registros de consumo de electricidad en los medi-
dores relacionados con la temperatura externa se
muestra en la Figura 1. La información disponible
se ha representado utilizando los identificadores
para cada vivienda descritos en la tabla 1 (ID), para
tem, which is unusual in the city. Butane is used for
DHW and cooking.
4.2. Energy consumption information from
meters
Energy consumption on Madrid housing is mostly
based on electricity and natural gas. Gasoil and Bu-
tane are not easy to quantify precisely because its
supply is based on demand.
4.2.1. Electricity consumption
Electricity supply in Madrid housing has nearly a
100% of implantation; every dwelling on the sam-
ple is connected to the grid. Information about con-
sumption from smart meters is available for 12 hou-
sing units placed on 6 different buildings. The plot
of the registers of electricity consumption on the
meters related to external temperature is shown at
the graph (Figure 1). Available information has been
represented using the identifiers for each dwelling
described in table 1 (ID), to guarantee anonymity.
Data has been provided by the electricity company
for the last three years, approximately every month.
4.2.2. Natural gas consumption
10 of the 19 dwellings of the sample have an insta-
llation for natural gas supply. Information for natural
gas consumption is available for 6 housing units pla-
ced on 4 different buildings. Plotting of monthly ave-
rage gas consumption related to external tempera-
ture is shown at Figure 2. Information is provided by
the gas company for the last two years. The raw data
represent meter readings on different dates and pe-
riods for each dwelling, so daily consumption had to
be calculated, extrapolated from bills.
The 2 dwellings form building 1 (dwelling 1.2 and
Figure1.
Registers for electricity
consumption recorded on
the meters (3 years).
97
Fernando Martín-Consuegra
Figure 2.
Registers for natural gas
consumption recorded on
the meters (2 years).
garantizar el anonimato. Los datos han sido propor-
cionados por la compañía de electricidad durante los
últimos tres años, aproximadamente cada mes.
4.2.2. Consumo de gas natural
10 de las 19 viviendas de la muestra cuentan con
una instalación de suministro de gas natural. La in-
formación para el consumo de gas natural está dis-
ponible para 6 unidades de vivienda ubicadas en 4
edificios diferentes. El gráfico del consumo mensual
promedio de gas relacionado con la temperatura
externa se muestra en la Figura 2. La compañía de
gas proporciona la información correspondiente a
los últimos dos años. Los datos sin procesar repre-
sentan lecturas de medidores en diferentes fechas y
períodos para cada vivienda, por lo que el consumo
diario se tuvo que calcular extrapolando los datos de
las facturas.
Las 2 viviendas del edificio 1 (vivienda 1.2 y 1.3, en
azul) tienen un comportamiento similar, al igual que
la vivienda 6.4. Registran un patrón de consumo que
indica un cierto uso de energía para la calefacción
en invierno. Las viviendas 3.2 (amarillo) y 5.3 (gris)
muestran un comportamiento diferente y los altos
consumos de gas aparecen desplazados en el tiem-
po hacia la primavera y el verano. La razón podría ser
que los medidores inteligentes aún no están instala-
dos en estas viviendas y la compañía de suministro
de energía alterna en su facturación estimaciones
de consumo con lecturas de contador de pasos. Esta
circunstancia dificulta el uso de esta información
para evaluar la distribución mensual del consumo en
esos casos. La vivienda 4.2 (rojo) utiliza una cantidad
muy baja de gas natural, ya que cuenta con calefac-
ción colectiva urbana que proporciona calefacción
en invierno. Estos casos registran un bajo consumo
y un triple suministro de energía (electricidad, gas
natural y gasóleo).
1.3, in blue) have similar behavior, so does dwelling
6.4. They register logical energy consumption that
indicates a certain use of energy for space heating
in winter. Dwellings 3.2 (yellow) and 5.3 (grey) show
a different behavior on the use of energy and high
gas consumptions appear displaced on time: during
spring and summertime. The reason could be that
smart meters are not yet installed on these dwe-
llings and energy Supply Company would be alter-
nating consumption estimates with infrequent step-
counter readings on their billing. This circumstance
makes it difficult to use this information for energy
assessment on those cases. Dwelling 4.2 (red) is
using a small amount of natural gas. Some dwellings
with natural gas implantation have also district hea-
ting providing space heating in winter, as in case 4.2
(red). These cases register a low consumption and a
triple energy supply (electricity, natural gas and ga-
soil fuel).
4.2.3. Aggregation of gas and electricity
consumptions
Total consumption of energy sources in Madrid in-
cluding electricity and natural gas (when installed) is
found on 4 housing units from 3 buildings. Highest
energy consumptions are registered in most cases
during winter, when external temperature is low.
Dwellings registering much lower energy consump-
tion than the average dwelling may not be meeting
a minimal comfort, except dwellings heated by ga-
soil (red lines). These dwellings have very low ener-
gy consumption registered in the meters, since no
information is available about gasoil consumption.
The total consumption on those dwellings is impos-
sible to assess by this methodology.
98 Consumo energético y brecha de confort en viviendas sociales de Madrid, a través de información de contadores inteligentes y encuestas
4.2.3. Agregación de consumos de gas y
electricidad
El consumo total de fuentes de energía, incluida la
electricidad y el gas natural (cuando existe) se ha po-
dido recopilar en 4 unidades de vivienda de 3 edifi-
cios. El mayor consumo de energía se registra en la
mayoría de los casos durante el invierno, cuando la
temperatura externa es baja. Las viviendas que re-
gistran un consumo de energía mucho menor que
la vivienda promedio pueden no estar en situación
de confort, excepto las viviendas calentadas con ga-
soil (líneas rojas). Estas viviendas tienen un consumo
de energía muy bajo registrado en los medidores, ya
que no hay información disponible sobre el consu-
mo de gasoil. El consumo total en esas viviendas es
imposible de evaluar con esta metodología.
4.2.4. Consumo detallado registrado por
vivienda
Se obtiene información completa para las viviendas
1.1, 1.2, 1.3, 3.2, 5.3 y 6.2. Como ejemplo, la Figura 4
representa el consumo total de energía registrado
por medidores inteligentes en las viviendas 1.1, 1.2
y 6.2. Los registros de electricidad están representa-
dos por la línea roja. La línea “total” (en negro) incluye
la agregación de los consumos de gas y electricidad
(en kWh). En viviendas sin instalación de gas las lí-
neas son coincidentes.
La vivienda 1.1 usa electricidad para todo su equi-
pamiento. El bajo consumo registrado indica que
no es probable que se esté alcanzando una situa-
ción de confort durante el invierno, tal como se
declara en la encuesta. Está ocupado por una sola
persona que podría estar sufriendo una situación
de pobreza energética.
La vivienda 1.2, ocupada por 4 personas, cuenta
con una instalación de gas natural para calefac-
ción. La agregación de consumos (kWh) se puede
Figure 3.
Aggregation of electricity
and natural gas consumption
recorded by supply
companies.
4.2.4. Detailed consumption registered per
dwelling
Complete information is obtained for dwellings
1.1, 1.2, 1.3, 3.2, 5.3 and 6.2. As an example, Figure
4 represents total energy consumption registered
by smart meters in dwellings 1.1, 1.2 and 6.2. Elec-
tricity registers are represented by the red line. The
“total” line (in black) includes the aggregation of gas
and electricity consumptions (in kWh). In dwellings
without gas installation the lines are coincident.
Dwelling 1.1 uses electricity for all its equipments.
The low consumption registered indicates that
comfort is not likely to be met during the winter,
as it is also declared on the survey. It is occupied
by one single person that could be suffering from
an Energy Poverty situation.
Dwelling 1.2, occupied by 4 persons, has a natural
gas installation for heating. The aggregation of
consumptions (kWh) can be done only from the
past two years. The data shows peaks of gas con-
sumption during the winter, when external tem-
peratures are low. This dwelling could be meeting
comfort during the winter, as it is declared on the
survey, but declares a lack of comfort during the
summer.
Dwelling 6.2 is also occupied by 4 persons. It uses
electricity for all supplies, the same as dwelling
1.1, but the register of consumptions evidences
a very different situation, with high amounts
of energy used for heating, and some peaks of
electricity consumption during the summer. This
situation is probably caused by the use of refrige-
ration systems when temperatures are highest.
Even though, in the surveys a lack of comfort is
declared during the summer and the winter on
this dwelling.
99
Fernando Martín-Consuegra
Figure 4.
Examples of registered
consumption for dwellings
1.1, 1.2 and 6.2.
hacer solo desde los últimos dos años. Los datos
muestran los picos de consumo de gas durante
el invierno, cuando las temperaturas externas son
bajas. Esta vivienda podría estar en confort du-
rante el invierno, como se declara en la encuesta,
pero declara una falta de comodidad durante el
verano.
La vivienda 6.2 también está ocupada por 4 per-
sonas. Utiliza electricidad para todos sus suminis-
tros, al igual que la vivienda 1.1, pero el registro
de consumos evidencia una situación muy dife-
rente, con altas cantidades de energía utilizada
para calefacción y algunos picos de consumo de
electricidad durante el verano. Esta situación es
probablemente causada por el uso de sistemas
4.2.5. Other energy sources consumption
Other sources of energy, besides electricity and na-
tural gas, found in the sample are
Gasoil for heating in the case of dwellings with
district heating service (4.1, 4.2, 4.3, 4.4), and on
case 6.1 that has an individual installation of ga-
soil for space heating and DHW.
Butane is mostly used for instant DHW and coo-
king.
Both butane gas and gasoil are not registered by
smart meters so they are impossible to quantify by
this research.
100 Consumo energético y brecha de confort en viviendas sociales de Madrid, a través de información de contadores inteligentes y encuestas
de refrigeración cuando las temperaturas son
más altas. Aun así, en las encuestas se declara una
falta de confort durante el verano y el invierno en
esta vivienda.
4.2.5. Consumo de otras fuentes de energía
Otras fuentes de energía, además de la electricidad y
el gas natural, que se encuentran en la muestra son:
Gasóleo para calefacción en el caso de viviendas
con servicio de calefacción urbana (4.1, 4.2, 4.3,
4.4), y en el caso 6.1 que tiene una instalación in-
dividual de gasóleo para calefacción de espacios
y ACS.
El butano se usa principalmente para ACS instan-
táneo y para cocinar.
Tanto el gas butano como el gasoil no están registra-
dos por medidores inteligentes, por lo que es imposi-
ble cuantificarlos con este método.
5. Discusión
La comparación de los datos de consumo con la
información de las encuestas permite asociar el
consumo de energía con los electrodomésticos y
las fuentes de energía que se encuentran en cada
vivienda.
El análisis del consumo de energía para la calefac-
ción debe ser diferente según el sistema de calefac-
ción y la fuente de energía (individual mediante gas
natural, eléctrica o colectiva). El análisis del consumo
para refrigeración se puede realizar contabilizando el
consumo excesivo de electricidad durante el verano.
Las situaciones de falta de confort declaradas son
subjetivas y no significan necesariamente ningu-
na evidencia científica. Por ejemplo, la vivienda 6.2
tiene uno de los mayores consumos de electricidad
en verano e invierno. Esta vivienda tiene calefacción
eléctrica, por lo que este alto consumo debe ser un
indicador de confort en invierno porque hay una
gran cantidad de calor entregado a la casa. Además,
durante el verano hay picos en el consumo en los
momentos más cálidos, lo que indica un consumo
de refrigeración. Sin embargo, esta vivienda decla-
ra tener frío durante el invierno y calor en verano.
Se podría estimar la demanda energética teórica de
calefacción y refrigeración para el confort térmico a
fin de compararla con el consumo de energía real y
encontrar la brecha entre ambos (Palma et al., 2019).
Además, la monitorización de la calidad ambiental
en interiores podría ser útil en este tipo de casos,
para aclarar la situación.
6. Conclusiones
La información proporcionada por los contadores
inteligentes es relevante para la investigación, pero
5. Discussion
Comparison of registered consumption data with in-
formation from surveys permits to associate energy
consumption with the appliances and energy sour-
ces, and with socioeconomic profiles of occupants
and dwelling type.
Analysis of energy consumption for space heating
must be different depending on the heating system
and energy source (individual with natural gas, indi-
vidual with electricity and collective heating). Analy-
sis of consumption for cooling can be done by ac-
counting the excess electricity consumption during
the summer.
Situations of declared lack of comfort are subjective
and do not mean necessarily any scientific eviden-
ce. For example, dwelling 6.2 has one of the highest
electricity consumption in summer and winter. This
dwelling has electric heating, so this high consump-
tion should be an indicator of comfort because there
is a high amount of heat delivered to the house. Also,
during the summer there are peaks on consumption
on the warmer moments, which is an indicator of
certain consumption for space cooling. However,
this dwelling declares to be in discomfort during
the winter and also in summer. A building typology
approach could be applied to estimate the heating
and cooling theoretical energy demand for thermal
comfort in order to compare with the real final ener-
gy consumption and find the gap between them
(Palma et al., 2019). Also, Indoor Environmental Qua-
lity monitoring could be useful in this kind of cases,
in order to clarify the situation.
6. Conclusions
The information provided by the smart meter is rele-
vant to research, but it is too hard to get. Despite the
growing rollout of smart meters across EU, nowa-
days, in Spain, smart meters’ information is not of
easy access for researchers. By one hand, they are
not yet installed in every dwelling, even though the
rate of installation is increasing quickly, and soon
their implantation will be generalized. On the other
hand, researchers need an authorization form the
occupants in order to use their private data, and not
every user is willing to do that. The energy compa-
nies only have the obligation to provide smart me-
ters registered data to the trading companies
Smart meter information is more accurate than
step-counter meters. The first has regular informa-
tion of every month’s and even below hourly infor-
mation about consumption that can be used for
seasonal consumption interpretation. Step counter
meters are not regularly updated, they can include
some consumption estimations that could be higher
or lower than real consumption and are adjusted on
the following months, which makes the interpreta-
tion of results confusing.
101
Fernando Martín-Consuegra
resulta difícil de obtener. A pesar del creciente des-
pliegue de medidores inteligentes en la UE, en Espa-
ña la información no es de fácil acceso para los in-
vestigadores. Por un lado, aún no están instalados en
todas las viviendas, a pesar de que la implantación
está aumentando rápidamente, y pronto se genera-
lizará. Por otro lado, los investigadores necesitan una
autorización de los ocupantes para usar sus datos
privados, y no todos los usuarios están dispuestos a
hacerlo. Las empresas de energía solo tienen la obli-
gación de proporcionar los datos registrados a las
empresas comercializadoras.
La información de los contadores inteligentes es
más precisa que en el contador de pasos. El prime-
ro tiene información en detalle para cada hora. Esta
información se puede utilizar para la interpretación
del consumo estacional. En cambio, los contadores
de pasos no se actualizan periódicamente, pueden
incluir algunas estimaciones de consumo que po-
drían ser más altas o más bajas que el consumo real
y se ajustan en los meses siguientes, lo que hace que
la interpretación de los resultados en esos casos sea
confusa.
El consumo de energía podría no estar necesaria-
mente relacionado con la percepción de confort de
los usuarios en la muestra analizada. De todos mo-
dos, se necesitan más casos para establecer conclu-
siones generales, como se ha hecho en Portugal.
La estimación de la pobreza energética a través de
los datos de consumo podría ser posible cuando
se compara con una estimación de la demanda de
calefacción y refrigeración. Las viviendas que regis-
tran un consumo de energía mucho menor que la
demanda de energía (sin una fuente de energía al-
ternativa) podrían estar en riesgo de pobreza ener-
gética. Al menos, se podría concluir que no están sa-
tisfaciendo el confort energético y existe una brecha
entre las necesidades y el consumo de energía.
La monitorización del consumo de energía en las
viviendas permite desagregar los diferentes usos de
la energía, pero financiarla supone un alto costo. La
comparación de este análisis con los resultados futu-
ros de la campaña de monitorización anual será in-
teresante para probar si la información contenida en
los medidores inteligentes es suficiente para evaluar
el consumo de energía en las viviendas.
Energy consumption could not be necessarily rela-
ted to comfort perception of the users in the sample
analyzed. Anyway, more cases are needed to esta-
blish general conclusions as in Portugal
Energy poverty estimation through metering con-
sumption could be possible if compared to an es-
timation of space heating and cooling theoretical
energy demand. Dwellings registering much lower
energy consumption than energy demand in the
building (without an alternative energy source)
could be at risk of energy poverty. At least, it could be
concluded that they are not meeting energy comfort
and there is a gap between energy needs and energy
consumption.
Monitoring of energy consumption in dwellings per-
mits to disaggregate different energy uses, but it
means a high cost to finance research. The compari-
son of this analysis with the future results of one year
monitoring campaign will be interesting to try to
prove if the information contained on smart meters
is enough in order to assess energy consumption on
residential units.
102
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Agradecimientos / Aknowledgements
The authors thank the support of the following institutions:
To Ministerio de Economía y Hacienda de España, for its finance support through Programa Estatal de
I+D+i to the Project: BIA-2017-83231-C2-1-R. HabitaRES: Nueva herramienta integrada de evaluación
para áreas urbanas vulnerables. Hacia la autosuficiencia energética y a favor de un modelo de habitabili-
dad biosaludable.
João Pedro Gouveia acknowledge and thank the support given to CENSE by the Portuguese Foundation
for Science and Technology (FCT) through the strategic project UID/AMB/04085/2019.
To the program of European Cooperation in Science and Technology COST Action 16232 - ENGAGER
(2017-2021): European Energy Poverty: Agenda Co-Creation and Knowledge Innovation, for its funding for
a Short Term Scientific Mission in Nova Universidade de Lisboa
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Daily electricity consumption profiles from smart meters are explored as proxies of active behavior regarding space heating and cooling. The influence of the environment air temperature (multiple maximum and minimum daily thresholds) on electricity consumption was explored for a final sample of 19 households located in southwestern Europe (characterized by hot, dry summers and cool, wet winters), taking the full year of 2014. Statistical analysis of the deviations from hourly average electricity consumptions for each temperature thresholds was performed for each household. Firstly, these deviations could act as proxies highlighting possible lack of thermal comfort on space cooling, and partially on space heating, supported by door-to-door survey data, on socio-economic details of occupants, buildings bearing structure and equipment's ownership and use. Secondly, meaningful differences of consumers’ behavior on electricity consumption pattern were identified as a response for space heating and cooling to the environment air temperatures thresholds. Additionally, statistical clusters of active and non-active behavior groups of households were assessed, showing the electricity use for space heating. This paper illustrates the importance of the widespread use of smart-meters data on the increasingly electrified buildings sector, to understand whether and how thermal comfort could be achieved through active climatization behavior of its occupants. This is particularly important in regions where automatic HVAC systems are almost absent.
Proyecto SECH-SPAHOUSEC. Análisis del consumo energético del sector residencial en España
IDAE, 2011. Proyecto SECH-SPAHOUSEC. Análisis del consumo energético del sector residencial en España. Informe Final.
Censo de Población y Viviendas de
  • Ine
INE, 2001. Censo de Población y Viviendas de 2001.
Discontinuidad y peculiaridad de los datos de vivienda del "censo" de
  • J M Naredo
Naredo, J.M., 2014. Discontinuidad y peculiaridad de los datos de vivienda del "censo" de 2011". El censo de 2011 en el marco de la experiencia censal en España.