“Eficiencia energética en edificios. Proyecto S4ECoB” “Energy

Transcription

“Eficiencia energética en edificios. Proyecto S4ECoB”
“Energy Efficient Buildings: S4ECoB Project”
Madrid, 11 Marzo 2015 / March 11 2015
Schneider Electric España
Calle Hilanderas, 15
Getafe - Madrid - Spain
Agenda
10.00
Registro asistentes / Registration
10.30
Bienvenida / Welcome
Adolfo Barroso, Schneider
Introducción / Introductory Session
10.35
BMS y la eficiencia energética en un edificio / BMS and Energy Efficiency in a Building
Joaquim Daura, Sales Manager of Energy & Sustainability Services - Schneider Electric
Proyecto S4ECoB / Sound for Energy Control of Buildings Project
11.20
Proyecto I+D / S4ECoB project. Objectives and development of the project
David Lanceta, Ingeniero I+D Solintel
11.50
Tecnología de sensores acústicos / Acoustic Sensors Technologies
Stefan Goetze, Fraunhofer IDMT
12.30 – 12.45 Pausa café / Coffee break
12.45
Método de control de instalaciones HVAC mediante la ocupación del edificio en el
Aeropuerto de Linate / HVAC Occupancy control methodology for Linate Airport
Blaise Kelly, AIT Austrian Institute of Technology
Puesta en común / Round Table Session
13.10
I+D en la tecnología del BMS en edificios. Aplicaciones prácticas /
R&D of BMS Technology. Practical applications
Schneider, Solintel, Fraunhofer, AIT y Corio
13.55
14.00
Clausura /Closure
Aperitivo / Aperitif
Hosted by Schneider Electric in Getafe (Madrid).
Las intervenciones serán en castellano o con traducción simultanea.
S4ECoB has received funding from the European Union’s Seventh Programme for research, technological
development and demonstration under grant agreement No 284628.
Fraunhofer Institute for Digital Media Technology IDMT
Project Group Hearing, Speech and Audio Technology
Oldenburg, Germany
Acoustic Sensing Technologies
Energy Efficient Buildings Workshop, S4ECoB
Madrid, 11.03.2015
Dr.- Ing. Stefan Goetze
Tel: +49 (0) 441 2172-432
s.goetze@idmt.fraunhofer.de
© Fraunhofer
Acoustic Event Detection - Motivation
© Fraunhofer
Acoustic Event Detection and Classification
…
…
© Fraunhofer
Acoustic Event Detection and Classification
Clapping
5577
5577
3746
3746
2493
2493
Frequenz / Hz
Frequenz / Hz
Cough
1635
1047
1635
1047
645
645
370
370
181
181
52
52
500
1000
1500
Zeit / ms
2000
2500
200
5577
5577
3746
3746
2493
2493
1635
1047
800
1000
1200
1635
1047
645
645
370
370
181
181
52
52
100
© Fraunhofer
600
Zeit / ms
Telephone
Frequenz / Hz
Frequenz / Hz
Knocking
400
200
300
400
500
600
Zeit / ms
700
800
900
1000
200
400
600
Zeit / ms
800
1000
1200
Cough Detection – A simple classifier
Simple classifier: SelfOrganizing Map (SOM)
Distance comparison to
previously trained models
Ereignisraum
© Fraunhofer
Noise and Acoustic-Scene Classification
Challenges
Detection and recognition of
complex acoustical events
Feature extraction based on the
human auditory model
Simple training of the recognizer
Classification of unknown events
Technologies
Description based on auditory
models
psychophysical parameters
© Fraunhofer
Acoustic Sensing Technologies
Acoustic Event
Detection
Comfort
Energy
Efficiency
Condition
Monitoring
Speech
Recognition
Control in harsh
environments
Industrial
Automation
Assistive
Systems
Safety &
Security
Traffic
Monitoring
Acoustic Sensing Technology has a large variety of applications and use
cases
Fraunhofer IDMT involved in numerous related research and industry
projects covering tasks at every stakeholder level, i.e. specification,
research and development, integration and testing, evaluation,
dissemination and exploitation
© Fraunhofer
System Overview
...
General system overview
Low-level analysis
Mid-level representation
High-level sematics
Preprocessing
Stage
Acoustic Event
Detection
Statistical
Modeling
Notification &
Actions
e.g. VAD, denoising,
localisation,
beamforming,
Feature Extraction
e.g. sirens, traffic &
vehicles, speech,
occupancy levels,
screams, electronic
devices, ...
Instantaneous
classification &
long-term
monitoring
e.g. lighting
systems, BMS,
alarm systems,
mobile end-devices,
...
Action
Increasing accuracy
highly adaptable to other scenarios due to modular structure in each
stage, fully automated, non-obtrusive
respects privacy issues and does not store any contextual information at
any point of time
→ higher end-user acceptance e.g. than video surveillance
© Fraunhofer
Sensoric frontend
Microphone / Arrays
Sensor frontend development and
prototyping is a core competency at
Fraunhofer IDMT, Oldenburg
Single- and multichannel-setups,
wireless sensor nodes available
Different geometries available
Line arrays
Spear-Arrays
Circular arrays
Spherical arrays
Latest development: Raw Audio
Streaming via TCP/IP-PTP (up to
8x10 channels, synchronized)
© Fraunhofer
Acoustic Processing Unit
Components
Hardware
BeagleBoneBlack with
Audio Cape
Custom made metal housing
High performance
microphone + preamplifier
Software
Audio Signal pre-processing
algorithms
Acoustic Event Detection and Speech Recognition Framework
Single and multichannel-support
Adaptable interfaces for technological backend connection
© Fraunhofer
Deployment Examples
Principe Pio shopping mall, Madrid,
Spain
Maremagnum shopping mall
Barcelona, Spain
Linate Airport, Milan, Italy
MANDAT international offices,
Geneva, Switzerland
Various care homes across Germany
and Switzerland
Child day care, Frankfurt, Germany
City of Santander, Spain
Automotive implementations
© Fraunhofer
Selected Projects on
Acoustic Sensing
Technologies
© Fraunhofer
Cough Classification Hotline aka Hustentelefon
(Industry)
Goal
Acoustic event detection and
classification via phone hotlines for
distinguishing „dry“ and
„productive“ coughs.
System integration and hotline
establishment.
Partner
Petersen & Partner Pharma
Marketing
Boehringer Ingelheim
Try out your self:
Telephone: +49 8 00 / 00 07 178
Iphone / Android App: Silometer Husten Tester
© Fraunhofer
SonicSentinel (BMBF KMU Innovative)
Acoustic Monitoring and Emergency Detection in Care Environments
Goal
Intelligent Acoustic Monitoring &
Emergency Detection for
Retirement Homes and Hospitals
Technologies IDMT
Automatic event & speech
recognition
User interfaces for older persons
Reminder functions
© Fraunhofer
Data Aquisition & Basis
18290 hours of audio material
gathered, 10000 hours have been
annotated manually
4825 individual acoustic events
distributed over 17 classes
Acous tic
Ev ents
Breathing
Breaking glass
Coughing
Moving
furniture
Whimper
Whining
Snoring
Calling for nurse
Falling down
Door slam
Screaming
Running water
tap
Suffocation
Shouting for
help
Moaning
Speech
singing
Ranking
(0 -not
im portant;
5 – im portant)
1,67
4,67
4
3
Alarm
Alarm
Alarm
False alarm
3,67
4,67
3,33
4
Alarm
Alarm
False alarm
Alarm
4,67
1,33
4,67
1,33
Alarm
False alarm
Alarm
Alarm
4,67
4,67
Alarm
Alarm
1,67
1,33
1,33
Alarm
False alarm
False alarm
Category
Ty pical acous tic ev ents in healthcare env ironm ents
incl. category and ranking.
© Fraunhofer
Results
Evaluation at an SNR of 20dB
and -3dB,
High detection performance
for static acoustic events and
artificial noises
Detection performance
decreases with decreasing SNR.
Confus ion Matrix for the acous tic ev ent
detection ex perim ent at 20dB S NR
Challenge with AED approach:
Human made sounds vs.
speech, radio, TV and singing
sounds (intraclassmemberships).
Solution: Integration of
Speech Recognizers
© Fraunhofer
Confus ion Matrix for the acous tic ev ent
detection ex perim ent at -3dB S NR
MOBECS (AAL-JP-2011-4)
Mobility and Emergency Call System
Goal
Increase mobility and safety of
older people
navigation und localization of
persons
Emergency calls and assistance via
a service center
Technologies IDMT
Acoustic event and emergency
detection via sensors in intelligent
fabrics and mobile devices
Self-learning algorithms
© Fraunhofer
S4ECoB (EU FP7-2011-NMP-ENV-ENERGY-ICT-EeB)
Sounds for Energy Control of Buildings
Goal
Energy savings by means of acoustic
scene analysis
Obtain parameter „occupancy“. How
many persons are doing what in
buildings?
Use obtained information to manage
HACV systems
Technologies IDMT
Acoustic position estimation and
tracking / spatial filtering
Acoustic Event Detection and
Classification
Semi-automatic learning algorithms
© Fraunhofer
Results: Maremegnum, Barcelona, Spain
Deployment of 3 microphone arrays
(ASU) in the shopping mall
Ground truth for occupancy level
estimation: people counting system
Audio database: 30 days @ 3 channels
Overall accuracy currently at 70%
Label_4
1
4
0.9
num ber of v is itors
0
96
97
498
499
1716
1717
2996
2997
λ
act. occupancy level
0.8
Label_4
occ0
occ1
occ2
occ3
occ4
3
0.7
0.6
2
0.5
0.4
1
0.3
0.2
Upper limits of number of people for each occupancy level
0
0.1
0
[LLM14] Eugenio Roanes-Lozano, Luis M. Laita, Eugenio Roanes-Macıas, An accelerated-time
simulation of departing passengers’flow in airport terminals, MatCom, 2014
1
2
3
est. occupancy level
4
0
© Fraunhofer
Results: Flow and condition metering in pipes
Passive acoustical flow-measurement using noise emission of different
tube-fittings was successfully investigated
Linear correlation between fluids flow and frequency of distinct
signal produced by solenoid-valve was found
Passive acoustical measurement of flow is possible, using the noise
emitted at a cross-section-tapering
Results: Joint Occupancy level estimation
and acoustic source localization
Implementation of source
localization algorithms and
visualization tools
© Fraunhofer
EAR-IT (EU FP7-ICT-2011-8)
Experimenting Acoustics in Real environment using Innovative Testbeds
Description & Motivation
Availability of wireless sensor networks from the FIRE initiative,
SmartSantander and HOBnet in particular
Those testbeds contains sensor arrays already and a complete
infrastructure for applications
© Fraunhofer
EAR-IT Indoor Usecases
Environment and Comfort (Speech Command Recognition)
Security & Monitoring (Acoustic Event Detection)
Energy Efficiency (Presence Detection for Office Environments)
EAR-IT Outdoor Usecases
Siren Detection, Tracking & Visualization
Traffic Density Monitoring & Visualization
© Fraunhofer
Results – Traffic Density Monitoring
Estimate the traffic density, based on environmental noise (density
level from 0-5)
Find correlation between the noise and the traffic density
Dynamic traffic conditions (0-65 car/min)
© Fraunhofer
S irenen detection in traffic
Automatic detection and
recognition of sirens in
traffic situations
Detection also possible
out of visual focus
© Fraunhofer
Emergency vehicles
Honk
Railway crossing
Tram / cable car
…
8000
8000
7000
7000
6000
6000
5000
5000
Frequency / Hz
Possible alarms
Frequency / Hz
Siren Detection
4000
4000
3000
3000
2000
2000
1000
1000
0
0
0.5
1
1.5
2
2.5
Time / s
3
3.5
4
4.5
0.5
1
1.5
2
2.5
Time / s
3
3.5
4
4.5
1
clean
0.5
0
-0.5
machine human
-1
0
10
20
30
40
50
60
time in seconds
Problems for humans:
Distraction
Acoustical isolation
Music
…
1
noise
0.5
0
-0.5
human machine
-1
0
10
20
30
time in seconds
© Fraunhofer
Experiment - MFCC + Filtering
Recognition rates for MFCCs with
different band pass filters for traffic
condition ‚normal‘ (solid line) and
‚rain‘ (dashed line) for fifferent
SNRs. Only ‚normal‘contained in
training.
Enhancement by filterung (green,
red)
SNR > -5 dB: accuracy > 90%
SNR > +5 dB: accuracy
~100%
Increasing robustness (see
‚rain‘; dahed)
© Fraunhofer
40
50
60
Smart Kita (BMBF – 60+)
Working place Kita, attractive for all ages
Goals
Reduction of physical, cognitive and
mental strain of elderly child care workers
due to noise, physical overload, too
condensed working processes and
inadequate recreation possibilities
Technologies IDMT
Development of an intelligent noise
management system
Auditory-motivated noise monitoring
with logging function
Analysis of noise pollution for
optimizing working processes
Automatic recognition of potentially
dangerous sounds and situations
© Fraunhofer
Automatic Speech Recognition for Smart Homes
Goals
Automatic speech recognition for control
of technical systems
Automatic speech recognition
Noise reduction / beamforming
Acoustic echo compensation
Current system
Keyword-spotter
Light, entertainment, shades, movies,
radio, etc.
© Fraunhofer
ALIAS (AAL-JP-2009-2)
Adaptable Ambient Living Assistant
Goal
Development of a robot platform
“Ambient Living Assistant”
AED/ASR interfaces for humanmachine interaction and
emergency detection
Technologies IDMT
Automatic event/speech
recognition
User interfaces for older persons
Reminder functions
© Fraunhofer
ALIAS (AAL-JP-2009-2)
Adaptable Ambient Living Assistant
Results
VoIP-Communication, Chat-tool M
Acoustic Event & Speech triggered
Emergency call N
Speech-enabled Media Server O
Speech-enabled reminder systemP
O
P
© Fraunhofer
M
N
Summary
Acoustic Sensing technologies have a wide range of applications both in
indoor and outdoor environments
Energy & Home Automation
Safety and Security
Monitoring applications
Implementations of signal pre-processing algorithms, acoustic event
detectors and reasoning strategies for embedded systems, PC platforms
and server setups available.
Adaptable interfaces for interconnection with technological back-ends.
General Event Detection Algorithms and Methods can be also applied to
Vibrations
Other sensor modalities (e.g., temperature, light, …)
Electrical consumption data
Bio signals
etc.
© Fraunhofer
Thank you very much for your
attention!
Dr.- Ing. Stefan Goetze
Tel: +49 (0) 441 2172-432
s.goetze@idmt.fraunhofer.de
© Fraunhofer
Eficiencia energética en edificios
Proyecto S4ECoB
David Lanceta Alonso, SOLINTEL
Workshop en Madrid – 11 de Marzo de 2015
Schneider Electric España, Getafe (Madrid), España
Motivación y concepto
• Actualmente la concentración de CO2 en la atmósfera ha alcanzado 400 ppm.
• Los expertos del Global Carbon Project (GCP) prevén en 20 años + 2ºC.
S4ECoB está cofinanciado por la Unión Europea dentro del 7º Programa Marco (FP7)
• No existe una solo tecnología o estrategia para reducir de manera
universal el problema del CO2. Cada país/región debe encontrar el
conjunto de medidas que se adecúe mejor a sus particularidades.
• Una de esas medidas, recomendadas por el GCP es incrementar la
eficiencia energética mediante cambios en la tecnología, en las
políticas, en el comportamiento humano y a través de la reducción de
la demanda energética. Esta acciones también conllevan un ahorro
económico a corto, medio o largo plazo, dependiendo de la medida.
•
•
•
Los edificios representan el 40%
del consumo energético final en
la UE.
El 70% de esta energía se utiliza
en calefacción, ventilación, aire
acondicionado e iluminación.
Mejorar la eficiencia de los
edificios
es
un
factor
fundamental para conseguir
reducir las emisiones de CO2 en
un 20% y mejorar la eficiencia
energética en un 20% para el
2020. (Objetivo UE)
Agricultura
•
En edificios modernos (generalmente no residenciales) se pueden encontrar
diferentes tipologías de sensores: video, temperatura, detección de gas, medición de
flujo, medición de iluminación, etc. Sin embargo los sensores capaces de medir
sonidos y ruidos no suelen formar parte del catálogo de sensores que se emplean
habitualmente en edificios.
•
Dada la importancia que los seres humanos damos al sonido en nuestros entorno, las
TIC (Tecnologías de la Información y Comunicación) basadas en la medición de sonido
pueden ser útiles para determinar el estado/situación actual del edificio, permitiendo
dar una respuesta adecuada por parte del BMS (Building Management System).
•
La creación de un sistema de sensores acústicos capaces de determinar el nivel de
ocupación y su posterior uso en sistemas BMS para incrementar la eficiencia
energética, es la principal finalidad del proyecto S4ECoB.
Objetivo
• El objetivo del proyecto es llevar a cabo un desarrollo sistemático y
una integración de herramientas en una unidad central que
monitoriza y procesa sonidos/ruidos para determinar con precisión
el nivel de ocupación y actividad dentro de un Edificio Inteligente.
• El nivel de ocupación se integrará en el BMS (Building Management
System) para optimizar el control del edificio (ECoB: Control of
Building).
Objetivos parciales
• Objetivo 1. Unidad Embebida de Procesado Acústico (APU). Plataforma HW/SW de procesado acústico, económica, de bajo consumo, adaptable, escalable. • Objetivo 2. Sensor de Ocupación.
Dispositivo fácil de instalar que monitoriza el nivel de ocupación en edificios y transfiere los datos en tiempo real al BEMO (Optimizador del BEM/BMS).
• Objetivo 3. Medición acústica de flujos de aire/agua
Sistema escalable de medición de flujos aire/agua integrable en BEM/BMS/BEMO
• Objetivo 4. Servidor de Procesado Acústico (APS) Procesa sonidos desconocidos (en tiempo diferido) y se encarga de la
capacidades de re‐entrenamiento (incluye el acceso a bases de datos de sonido).
Objetivos parciales
• Objetivo 5. BEMO: Optimizador del BEM/BMS Unidad central que conecta el BEM con los módulos de optimización.
• Objetivo 6. Manual de uso, instalación, integración y configuración. Guía de usuario que además contiene información para verificar la optimización.
• Objetivo 7. Demostradores: 3 edificios piloto.
Aplicación del sistema en 3 edificios reales.
• Objetivo 8. Información/Diseminación
Web, fórums, workshops, etc. S4ECoB está cofinanciado por la Unión Europea dentro del 7º Programa Marco (FP7)
Desarrollo del proyecto‐tecnologías
•
El sistema S4ECoB está compuesto de 2 diferentes tipos de tecnologías:
•
Tecnología del Sistema Acústico:
– 24 Micrófonos (agrupados en 3 arrays de 8 micrófonos)
– 3 Unidades Satélites de Audio (ASU) (1 array de micrófonos por ASU): Amplifica, digitaliza y codifica la señal analog. de micrófonos. – 1 Unidad de Procesado Acústico (APU)
•
Tecnología del servidor BEMO (Building Energy Management Optimizer) – 1 Servidor físico (o servidor virtual, si aplicable)
– 1 Infraestructura de Comunicación (OPC cliente‐servidor of tecnología BACnet)
Desarrollo del proyecto‐diagrama general
Desarrollo del proyecto‐conexiones
Desarrollo del proyecto‐Sistema Audio
ASU
MICROS
APU
Desarrollo del proyecto‐APU
Desarrollo del proyecto‐DEMO#1
CENTRO COMERCIAL MAREMAGNUM (Barcelona)
•
•
•
•
•
•
Situado en el puerto de Barcelona
La superficie total del edificio es de 22.890 m2 Sobre 13 millones de visitantes al año
Dispone de sistemas centralizados de iluminación, ventilación y primario de acondicionamiento térmico para locales.
La zona común (mall) está abierta al exterior y por lo tanto no acondicionada térmicamente.
Pertenece a la empresa CORIO
CENTRO COMERCIAL PRÍNCIPE PÍO (Madrid)
•
•
•
•
•
•
Situado en la parte oeste del centro de Madrid en la antigua estación de tren del Norte.
La superficie total del edificio es de 32.500 m2.
Sobre 12.5 millones de visitantes al año.
La zona común (mall) está conectada con la nueva estación de tren de Príncipe Pío, el intercambiador y dispone de garajes subterráneos. Dos sistemas HVAC independientes: uno en el edificio histórico y otro en el edificio nuevo.
Pertenece a la empresa CORIO.
AEROPUERTO INTERNACIONAL DE LINATE (Milán)
•
•
•
•
•
Aeropuerto situado a 4 km del centro de Milán, Italia. En 2009 tuvo 8 millones de pasajeros y se emplea principalmente para vuelos domésticos y vuelos internaciones de corto recorrido, estableciendo conexiones con las principales ciudades Europeas. La superficie de la terminal es de 75.000 m2 aunque el proyecto se centra en una pequeña zona de “salidas”. La electricidad y el calor provienen de una planta de cogeneración que hace que el aeropuerto sea enteramente independiente de la red externa.
Gestionado por la empresa SEA.
Desarrollo del proyecto‐Maremagnum
Zona de medición en Maremagnum
•
•
Los micrófonos se han instalado en la zona interior (mall) de Maremagnum.
Los lugares específicos fueron seleccionados después de la realización de un exhaustivo examen acústico del centro comercial.
Planta
1º
= Acceso rambla de mar
Planta
baja
•
Array de micrófonos desplegado en forma de bolas de Korziol en Maremagnum
Iluminación
•
•
•
•
El control del nivel de iluminación en función de la ocupación es interesante para el centro comercial.
Podrían proporcionarse diferentes iluminancias en función de la ocupación, con la disposición actual de circuitos (ahorro simulado 18%).
Problema: Luminarias CDM (Ceramic
Discharge Metal Halide) no modulan y se deteriorarían mucho más rápido con continuos encendidos y apagados.
Muy interesante en nuevos centros/luminarias compatibles.
Planta
baja
Anillo de condensación por agua de mar
Heat rejected in shops = f (Occupancy, Mall Temperature)
Heat Pump
HP
HP
Constant flow
Improved HP model
37kW
Constant flow
Improved Heat
Exchanger model
22kW
SEA WATER (Temperature varies with time of year) Maremagnum
Caso General
Inlet Water Temperature (ºC)
15 20 25 30 35
0.53 0.79 1.58
0.39 0.52 0.78 1.57
0.31 0.39 0.52 0.78 1.55
0.26 0.31 0.38 0.51 0.77
COP vs Condensing Temperature
COP
Water Flow (kg/s)
Condensing Temperature (ºC) 35
40
45
50
3.8
3.6
3.4
3.2
3
2.8
2.6
2.4
2.2
2
30
Datos de una bomba de calor de una tienda
•
35
40
45
50
55
Condensing Temperature (ºC)
Disminuir el caudal de las bombas selectivamente puede compensar el funcionamiento menos eficiente de las bombas de calor S4ECoB está cofinanciado por la Unión Europea dentro del 7º Programa Marco (FP7)
•
Propuesta teórica: variar la velocidad de la bomba del sec. en función de la carga de refrigeracion y temp. Agua de mar.
APU
Heat rejected in shops = f (Occupancy, Mall Temperature)
Occupancy
Mall Temperature
Heat Pump
HP
HP
•
•
Carga de refrigeración depende de temp. Exterior y ocupación.
Problema: El ensuciamiento del circuito primario hace que el caudal sea muy variable con el tiempo.
BEMO
BMS
VFD
Improved HP model
Acoustic Flow Meter
Constant flow
Improved Heat
Exchanger model
SEA WATER (Temperature varies with time of year) S4ECoB está cofinanciado por la Unión Europea dentro del 7º Programa Marco (FP7)
Ventilación
•
3 UTAs (sin baterías) introducen y extraen aire de los locales del centro. •
Propuesta teórica: accionar los ventiladores en función del nivel detectado de ocupación en el centro.
•
Sondas de CO2 se han instalado para validar el funcionamiento por ocupación
Ventilación
CO2 (ppm)
CO2 concentration in extraction air
2,000.00
1,800.00
1,600.00
1,400.00
1,200.00
1,000.00
800.00
600.00
400.00
200.00
0.00
12/11/2014 0:00
PEOR IDA4
IDA4
IDA3 IDA3
IDA4
IDA3
IDA3
IDA2 IDA3 IDA2
IDA3
IDA3
IDA3 IDA2
IDA1
IDA2 IDA2 IDA3
IDA2
17/11/2014 0:00
22/11/2014 0:00
27/11/2014 0:00
IDA2
02/12/2014 0:00
07/12/2014 0:00
Fan electricity consumption (50 Hz day, 30 Hz night) Power consumed by the fan (kW)
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
12/11/2014 0:00
17/11/2014 0:00
22/11/2014 0:00
27/11/2014 0:00
02/12/2014 0:00
07/12/2014 0:00
Optimización Ventilación BEMO
Almacenamiento
Ocupación
BMS estrategias de
control optimizado
BMS monitorización
de estrategias de
optimización
Arquitectura de red en Maremagnum
OPC=estándar de comunicación en el campo del
control y supervisión de procesos industriales, que
ofrece una interfaz común para comunicación que
permite que componentes software individuales
interactúen y compartan datos
Desarrollo del proyecto‐Príncipe Pío
Zona de medición en Príncipe Pío
•
•
Los micrófonos se han instalado en la zona interior (mall) del edificio “nuevo” de Príncipe Pío.
Los lugares específicos fueron seleccionados después de la realización de un exhaustivo examen acústico del centro comercial.
Sobre 1300 m2 + 1300 m2
Zonas a controlar en Príncipe Pío
•
•
Las máquinas autónomas rooftop
están integradas con sólo tres variables, marcha/paro, temperatura de consigna calefacción y temperatura de consigna de refrigeración
Estrategia compatible con el sistema: consigna variable en función del nivel de ocupación.
Thermal cooling energy (kWh/m2)
90
80
70
60
50
40
30
20
10
0
Reference case at 25ºC
Strategy 1, Occ. 2,3 => 25ºC, Occ. 1 => 26ºC
Strategy 2, Occ. 2,3 => 25ºC, Occ. 1 => 27ºC Simulación en modo refrigeración
Arquitectura de red en Príncipe Pío
Conclusiones
•
•
•
•
•
•
Los sensores de ocupación acústicos son una variante más de sensores que pueden ser integrados en un sistema BMS.
La capacidad de optimizar el funcionamiento de los sistemas de climatización/iluminación del edificio dependerá principalmente del tipo de sistema. Son más fáciles de integrar en obra nueva al poder diseñar el acoplamiento sistema control conjuntamente.
Son sensores multifunción que pueden dar inputs no solo a la climatización sino a otros sistemas: seguridad, incendios, etc.
Pueden identificar sonidos útiles para el gestor del edificio: rotura de cristal, sirenas, viento, actuando sobre el BMS (accionamientos, alarmas, etc.)
Responden a la ocupación antes que otros sensores (ej. CO2) lo que les confiere mayor capacidad de reacción.
Para más información
www.s4ecob.eu
david.lanceta@solintel.eu
HVAC Occupancy control methodology for Linate Airport
Methodology for reducing energy consumption using occupancy
data.
Blaise Kelly
Sustainable Buildings and Cities
Austrian Institute of Technology
11.03.15
Acoustic Sensing System (ASS)
2.6m
HVAC System

Hot water supplied
from onsite Natural
Gas CHP unit

Twin duct Variable Air
Volume (VAV) system

Supplies air with
average flow rate of
26,000 m3/hr at 22°C
24/7
11.03.15
3
Envelope Simulation

Simplified model of gate 1 – 8 area
created in TRNSYS

Simulated from July 11th
2013 to 8th December 2013

Actual (adjusted) weather data used along with flight
schedule data for occupancy
11.03.15
4
Occupancy Estimation
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Flight 1
Flight 2
90 80 70 60 50 40 30 20 10
Minutes before departure
Mins
90
85
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
% total 7.34% 10.49% 12.00% 12.36% 13.93% 13.87% 12.30% 12.65% 18.42% 19.59% 23.38% 26.12% 31.35% 27.09% 12.36% 11.19% 13.11% 11.14%
People
11
16
18
19
21
21
18
19
28
29
35
39
47
41
19
17
20
17
Time series estimation for a flight with 150 passengers boarding.
CO2 measurements

500 - 1400 litres per
second ventilation rate

Peaks correspond with
departures

Potential for flow
rate/fresh air reductions
11.03.15
6
Occupancy Estimation

Flight schedule data converted to 5
minute occupancy data based on
previous study of gate arrivals1
Occupancy Levels
300000
50000
Level
40000
200000
30000
Frequency
Frequency

Frequency distribution created
20000
100000
10000

74% of time gates not occupied

Occupied periods focused on 3 levels
created to maximise acoustic
detection system
People
0
0
1
1-15
2
16-50
3
51+
0
0
Number
of of
passengers
Number
passengers
1. Roanes-Lozano, E et el.. An accelerated-time simulation of departing passengers’ flow in airport terminals. Mathematics and Computers in Simulation 67, 163–172 (2004).
Final Set Points
June
Occupancy Level
11.03.15
December
Hot
Flow
Cold
Flow
Flow
Temp
Hot
Flow
Cold
Flow
Flow
Temp
Fresh Air
m3/hr
m3/hr
°C
m3/hr
m3/hr
°C
m3/hr
Occupancy Levels
0
150
200
21
1500
0
32
0
1
150
300
19
1000
200
29
450
2
300
1200
16
1200
300
28
1500
3
400
2000
15
1900
500
28
6000
Level
People
0
0
1
1-15
2
16-50
3
51+
8
Results

On average around 70% saving
estimated

Biggest savings for pre heat coil when
recirculation increased for low
occupancies
Cooling Energy
Main coil
Heating
Energy
Occupancy
control
June
Baseline
December
Occupancy
control
December
MWh
MWh
MWh
MWh
55.5
14.4
4.3
1
8.5
2.5
42.4
11.5
0.5
0.01
25.7
2.1
Fan Energy
1.1
0.3
12.7
12.7
Total
424.3
123.1
418.6
234.4
Pre-Heat
11.03.15
17.06.14
Baseline
June
9
Blaise Kelly MSc BEng
Philip Horn MSc Dipl.-Ing. (FH)
Austrian Institute of Technology
10
Occupancy profile using flight schedule

Flight schedule feed
into program

Output 5 minute
profile for every gate

Other areas around
can also be estimated
CO2 and recirculation
Recommended comfort criteria for specific applications taken from CIBSE Guide A
17.06.14
12
Minutes of discussion
Common workshop with Schneider Electric „Energy Efficient Buildings. S4ECoB project.”
Date:
March 11 2015
Venue:
Schneider Electric España, Calle Hilanderas 15, Getafe-Madrid, Spain
Participants:
see the list of participants at the bottom of this document
Pictures from the workshop
Presentation by Schneider Electric
Audience during the workshop
Fraunhofer IDMT presentation
Round table discussion
This project has received funding from the European Union’s Seventh Programme for research,
technological development and demonstration under grant agreement No. FP7-2011-284628
General information
-
-
-
The invitation considered gender issues. Especially female engineers were encouraged to
participate to the workshop. In the end it was possible to attract 6 female participants. In total
there were 57 participants during the workshop out of 80 received registrations.
Some attendees had to be refused due to the very high interest on the event.
The cooperation with Schneider Electric, the provider of the building management system of the
Maremagnum Shopping Centre, was seen as crucial for the organisation of the workshop. The
company has over 150.000 employees in 100 countries worldwide.
The discussion took place after presentations. Participants had the possibility to place questions
to the experts in the round table – representatives of Schneider Electric, Solintel, Fraunhofer
IDMT, AIT and Corio.
Discussion notes
1.
Question from an engineer responsible for installation purchases in a company:
How is it possible to distinguish the different noise levels?
-
2.
The installation depends on different environments. The system needs to be adapted to certain
conditions before its application.
Audio is not always the best solution for every application. Sometimes it could be better to use
other systems such as video sensing.
The costs of the system are also relevant. The S4ECoB system has low costs compared to other
systems.
Question from an engineer working with mechanical installations in a consulting company
Why is it not better to use CO2-sensors?
-
3.
The detection with the S4ECoB system is faster.
The aim is to reduce peaks in the energy consumption.
If the scenario changes fast the system is able to react quickly, e.g. in the case of theatre the
system can change very quickly after the presentation is over. The system is also able to deliver
prediction.
Schneider Electric has concrete problems in a theatre in Barcelona where the system doesn’t
react fast enough. For this purpose the S4ECoB system is suitable for application.
After the theatre is finished the ventilation can be adapted.
Question from an engineer working as a consultant in BREEAM and LEED building certifications
How can the system detect how the people behave (talking, shouting…)?
-
There are special algorithms in place that are able to distinguish the different noises.
In the case of airports an additional validation could be made by analysing the flight schedule.
4.
Question from an engineer working for a maintenance building company
How can a prediction be made?
-
5.
The prediction is based on historic data.
Question from an engineer working in a mechanical and electric installation company
When it comes to different cultures – can the system deal with different environments?
-
6.
The system is trained for different conditions before the use. The system comes with a basic
training. Subsequent training and re-training improves then the accuracy of the system.
Question from an engineer from a water purifying company
Concerning the European market for carbon emissions – how can a company compensate, in
case you are going to invest in energy consumption reduction measures, what is then your
income?
-
-
Economical impact in a company is the most important for the promotion of the service.
The education is important. The own consumption is important but how to save energy in a
hotel? That means that the economic issue is only a part. There should be an additional
education.
Importance of measurements in installations in every building to fix the level of consumption for
every building. Sometimes it’s the client to ask for measures in order to reduce the consumption.
We need to think about the whole life cycle. Even if the investment is high it could recover within
a certain amount of years.

“Eficiencia energética en edificios. Proyecto S4ECoB” “Energy

Transcription

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