1. business and industrial
2. energy
3. renewable energy

Development research of real-time monitoring and optimized
control for energy conservation and CO2 reduction of existing
buildings
Speakers:
Bumpei Magori 1, Tomonari Yashiro 2, Sako Hiroshi
3
1
Institute
2
Institute
3
Industrial Science, the University of Tokyo, Tokyo, Japan
Industrial Science, the University of Tokyo, Tokyo, Japan
Alter building Japan, Tokyo, Japan
Abstract:
Development/Methodology/Approach; Our first goal is to develop a real-time monitoring
system and locate wasted energy from energy-related data. The second goal is to develop
algorithms and control systems that optimize the control of M&E devices and systems in
order to minimize energy loss and achieve improved efficiency.
Findings; One is a real-time monitoring system and installation methodology for existing
buildings that have no sensors. Others include the development of procedures to analyze timeseries data of sensors, and algorithm.
Originality/Value –A) A procedural methodology for installing and monitoring sensors in
existing buildings and analyzing energy data. B) The algorithm for the optimization of control
systems.
Keywords, Real-time Monitoring, Visualization, Optimized Control, Energy Conservation,
CO2 reduction
Introduction/ Technical and social background
Fossil fuel consumption accounting for 30 to 50% of Japan, as the result of operation of
existing buildings. Effective use of non-renewable energy and, in order to reduce CO2
emissions, the effective use of non-renewable energy and the reduction of energy
consumption in existing buildings is necessitated important for us on a global scale. The
supporting technology and theories for reduction of energy consumption in existing buildings
have matured. Numerous technologies and productions for energy conservation have been
developed, diversifying the operating conditions and the physical aspects of existing
buildings. , Such diversification necessitates the continuous improvement of the "usage" on
the actual condition of the individual buildings. Therefore, the informational data of
“operating situations” and "usage" of individual property have to be collected and analyzed.
Currently, we do not have a standardized methodology for the collection and analysis of detail
time-series date related to energy consumption. Decisions for improvements of "usage" and
“operation” are generally made using intuitive judgment, without sophisticated engineering
studies and its resultant data. The purpose of this paper is to develop a strategy with real-time
monitoring for energy conservation and CO2 reduction in existing buildings and to optimize
the control for building equipment by using monitoring data. In addition, an attempt is to
develop a theory related to the real-time data monitoring method using a designed monitoring
system. This system was installed in the existing buildings, employed in the process of
collecting and analyzing monitored data in the field, and was used to improve the operations
of the existing buildings.
1.0 Research question and purpose / Development Research methodology
Our first goal is to develop a real-time monitoring system and locate wasted energy from the
monitored data. The second goal is to develop an algorithm and a control system that
optimizes the control of M & E devices and systems for minimization of energy loss and
1
achieves improved efficiency. In general, most buildings are not programmed to collect timeseries data or grasp quantative “space information”, “information related energy
consumption”, “information related to energy consumption”, and “operation data”. It is
necessary for energy management to keep the efficiency by using mechanisms designed to
process continues data, visualization and analysis. Our data management process and
applications include finding a variety of problems and items of waste energy about M&E
equipment from energy data. This methodology must easily identify problems and solutions
by using graph processing, statistical analysis and evaluations for appropriate energy
consumption. This mechanism captures the constantly changing environment and selects the
optimal solutions (control pattern and settings) for energy-saving and efficient energy use.
This development research methodology is implemented in an actual building group, and
verified whether integrated energy management is possible. (Table1-1)
Table 1-1 Purpose of development research
Purpose and the contents
①②
③
The
developed
system
can
be
implemented
inbeexisting
buildings
that facility
manager
isaalways
absent.
The
developed
system
and
methodology
can
adopted
organizations
that
do
not
have
full-time
energy
manager
This
methodology
is
introduced
into
organizations
with
many
stakeholders,
and
Verified
energy
saving
and data
CO
reduction.(shown
below
methodology
1)～6))
The
purpose
is
to
verify
the
flow
of
the
mechanism
(energy
collection, analysis, evaluation, associated with the solution and control algorithm for optimization.)
2
Development Research methodology
１）
２）
３）
４）
５）
Prototype
real-time
data
monitoring
and designed
of a general
model.To
clarify the elements
ofbuildings.
these technologies.
Arrange
the
issues
and
effects
of
"Energy
Visualization"
through
the
installation
in
existing
We propose a
mechanism
to
optimize
energy
use
to
identify
issues
from
case
studies,
and
identify
the
ideal
architecture.
Proposed
an architecture
that can
bearchitecture
implementedis allied
for effective
use as insocial
infrastructure,
and
used toofinvestigate
given
conditions
for
installation.
This
tp
the
building
its
actual
operation.
A
system
integrated
energy
management
is
designed
to
organize
its
effects
and
issues.
Optimize
the
mechanism
to take(Automatic
advantage control,
of the energy
data,control,
and energy
conservation
andcontrol,
CO reduction;
Developa
and
test
logic
and
algorithms
predictive
artificial
intelligence
etc.)
Propose
mechanism
associated with aInstalled
solution forin results
of data
analysisVerify
and energy
optimization
todevelopment
clarify the algorithm.
The
above
items1)～5）are
an
actual
operating.
the
contents
of
the
research, and
evaluate the feasibility.
2
2.0 Target Buildings and Organization
We selected the target buildings for our analysis. Because these buildings do not have
solutions for reducing the energy consumption and a demand control methodology.
・
・
・
・
・
No facility manager in the buildings. (Most small buildings do not employ staff for energy control and
management.)
Existing buildings which do not have sensors for energy management and those that do not have Building
Energy Management System (BEMS) and /or Energy Management System.( EMS)
Stakeholder complex organization
The building owner has several buildings.
The owned buildings are away from each other.
3.0 Designed system for data collectio
The proposed model is based on the systematic use of information supplied by the buildings.
(see Figure 3-1).
Data
collection
Facts
data
Action
Analysis
Concern
Interpretation
Control
Technical data
Economic data
Analysis is distorted by the lack of information
Figure 3-1 Well informed decision making model by using quantitative information
4.0 Visualization for what / Stake holders of energy management
The points of the designed system are below. (see Figure 4-1)
2
Web-based system. (date collection, visualization, analysis data) / Module function framework / Open source
(protocol, database and web application and software)
Information user
Manager
layer
Energy Information system
Data sending side
Monitoring system
Hotel
Admi and
financial
Facility
manager
Building
maintenance
Environmental
section
Office
Data Base
Research campus
Energy analysis server
Environmental impact analysis
Large-scale office building
Factory
Building
user
Distribution center
Figure 4-1 Real-time・data monitoring a conceptual diagram of the field
Represents the relationship between the stakeholders effective use of energy and energyrelated information. The left side of the figure indicates users of the information. The center
of the figure shows the information service providers that evaluate and analyze data
accumulation. The right side of the figure indicates a group of buildings for various
applications, and they are the originators of the data.The target is to apply the system in
buildings anywhere in the world. Information service providers present the information
stakeholders of the building by using the Internet. Information service providers can also
collect and retrieve information from the building and information necessitated by users, such
as information requires to change the usability of the building. Information service providers
collect the building-related energy information from sensors in the buildings. This system can
also issue commands and send the required information to the user. Information is available in
both directions. In addition, information may be transmitted and controlled by the information
service provider on behalf of or in the absence of another praty. In some cases, the
information users are both and stakeholder and partners. The same information is used for
different purposes and different display. Information service providers are required to have
the ability to display the results of their own purposes.
5.0 Module function framework (Sensors and the designed system)
Designed architecture for control and optimization of real-time monitoring. (see Figure 5-1)
It indicates function modules. These modules are different fields of technological advances
and industry.
・
・
・
・
・
・
“Black arrows” indicates sensing (data collection) to visualization flow.
“Blue arrows” show analysis information flow.
“Red arrows” show control information flow.
“Local” indicates onsite buildings and/or building related parties.
“Center” indicates integrated data storage and analysis system.
“Designed modules generally have open interfaces that use open protocol and are easily connectable.”
each other. Each module and software can be improved if the module functions are not able to adopt
this total system in the future.
3
Measurement System
①
Devise Control System
⑫
Building items with related to energy use
Ⅰ
⑪
②Sensors
Ⅱ
③Measurement devices
Ⅲ
equipment
ⅩⅠ
Control section
Ⅹ
Ⅷ
⑨Local data analysis section
Analysis System
④Data transfer section
ⅩⅢ
Ⅸ
ⅩⅤ
⑩Central data analysis section
ⅩⅡ
Ⅳ
Storage System
Local
⑤Receiver and real-time data collection
section
⑭Setting, Parameter, Demand
pattern data collection section
Center
ⅩⅣ
Ⅴ
Ⅴ
Display System
Optimize System
⑬ Optimize section
⑥Data Display section
Ⅵ
⑦Data Visualization section
Visualization System
Ⅶ
Local
⑧User
Figure 5-1 Development research whole architecture data flow and 7 function modules for visualization and
optimized control
Table 5-1 6 modules of visualization and optimized control
Measurement system module: This function module includes ①Energy related items ②Sensors ③Measurement device which
applies
physical
meaning to the data. ④Data transfer section which uses open protocol and translates the data into a generic
protocol
on
TC/PIP.
Storage System module: This function module includes ⑤Receiver and real-time data collection which have data servers and
data tables for each project
Display System module: This function module includes ⑥Data Display section which has a web server function.
Visualization System module: This function module includes ⑦ Data Visualization section and ⑧Users The visual section is a
web
browserfunction
on a general
PCdevice
and analog
some smart
devices connected through the Internet. Users themselves are able to include
this
module
as
the
controller.
Devise Control System module: This function module includes ⑪Control section and ⑫equipment. The Control section has a
special protocol that can adopt
many kinds of M&E equipment and system.
Analysis System module: This function module includes⑨ Local and ⑩Central data analysis sections that calculate and
compareindicators
the energy
consumption
of the best
results,
bothandpastCentral
and current.
The Local
dataincludes
analysisvalue,
sectionindicators
includesfortarget
value,
for
energy
consumption
and
efficiency,
data
analysis
section
energy consumption and efficiency.
5.1 The goals of this development are the followings.
・ The automatic visualization of obtained sensor data.
・ Share information with experts.
・ Energy audits and analysis using the time-series sensor data.
・ Control for optimization of energy use.
6.0 Real-time time-series data and analysis (Case study)
The following case study is analysis of the Field Study Building, public office building in
Yokohama. The Field Study Building was completed in 1999. This building has B3, F8, P1
4
stories and total area is 24,335 ㎡. The building type is ward office with a public hall (600
seats). The operation hours are from 8:45～17:00 on weekdays. This building is equipped
with multiple energy conservation systems, such as a high efficient absorption chiller、
variable water volume control, variable air volume control, cooling system that uses outside
air, high frequency lighting fixtures, a ice thermal storage system. “This building can easily
be called an energy conservation building’.
6.1 Monitoring analysis for Electrical energy demand.
Our procedure of our analysis is shown below.
１） Monitoring and analysis for power demand.
２） Verification for efficiency of heating and cooling sources.
３） Installing CO2 controller and verification of its effect.
Peak power demands are composed in only few hours, which are highlighted with the circle
line in 20 every hour; power demands from maximum order are extracted. It is possible to cut
the peak power demand form 908.8kW to 830kW, if the higher ranking 7 hours power
demand can be controlled. Our system should prepared to stop the equipment operation,
which will not affect building operation instantly, such as ventilation, when peak demand
would exceed 830kW.If this preparation and detail monitoring for power demand is
accomplished, 8.5% of the power demand can be cut. The peak power demand not only cuts,
also allows for more operation at local power plants.
1,000
920
36
900
900
34
880
32
)
W
k
(
d
n
a
m
e
D
r
e
w
o
P
N=2016
800
)
kW
( 860
nda
em840
D
er
820
w
o
P
700
600
500
400
300
200
100
30
28
26
800
24
780
22
20
760
0
1
201
401
601
801
1001 1201 1401 1601 1801 2001
Figure6-2 Order of Power demand (all data)
)
(℃
reu
ta
re
p
em
T
ir
A
dei
st
u
O
13 12
9
14 11 10 10 13 13
9
11 10 13 11 15 14 14 13 13 15
Hour
7/30 7/30 8/7 7/30 8/7 8/7 7/16 7/16 8/6 7/16 7/16 7/30 8/10 7/30 7/30 7/16 8/7 8/19 8/5 7/16 Month/day
Figure6-3 Order of Power demand (top 20 data)
7.0 Algorithm for optimized energy demand and energy saving control
7.1 Features of the theory
The following are the main features of the algorithm and its theory.
・ Discover the particular demand patterns for different buildings by using the energy
demand of real-time data.
・ Locate the highly relevant parameters according to the demand pattern.
・ Select control relevant to the identified demand pattern, effectively reducing energy
consumption.
7.2 Features of the developed algorithm (see Figure 7-1)
According to the CO2 sensor and the temperature and humidity sensor installed in the interior,
the building is considered comfortable.
・ From the created demand model that has been extracted from the historical monitoring
data, the energy demand can be predicted, calculated, and corrected.(see Table 7.2)
・ In the process of creating the demand model, we determined whether there is a need to
classify the model. At first, we make 24hour demand patterns and classify the following.
5
Whether changes in demand in different seasons.
Whether the demand changes during weekdays and holidays.
Whether the demand changes special events take place.
Data is then compared to identify whether there is any effect on the demand parameters.
Start
Calculate and predicted demand
model from by using given
Historical timeseries data
Correct the model
Correction coefficients:
Weather forecasting
・
Max, Low, and Av temperature of
the previous day
Probability and rainfall
Holiday weekdays or weekends
・
event day or not
・
・・
Created demand patterns
N
Compared with past
demand models
Y（Amount 95%≧
N
Event change level
Y
Determine the demand prediction
model
Select a number of energy-saving control patterns
<Electric power control>
<Control Energy>
Change the set temperature
・
Automatic-reporting and monitoring power ・
・
Control and CO2 concentration
demand
・
Control and inverter
・
Control and electric power demand
・
Control and airflow
・
Interval Control
・
Control and intermittent operation
・
Rotation control
Rotation control and operation
Determine the order of the control
patterns
Real time demand
data
Set the various parameters
N
Comparing the current value
and the predict value
Y
N
predict ≧current
Within +2.5%, -5.0%, 15min
Determine the increase of the
energy saving level
Y
Determine the rapid changes
in the outside temperature
and humidity
N
END
Y
(Figure 7-1 ) Optimized energy demand and energy saving control algorithm
6
Table 7.2 Parameters for demand model
Building property; Regional characteristics / Building orientation / Scale / Outer wall and window position and size
Building types; Office building / Commercial Building / Hotels. etc
Typical operation
operation form; Morning / Daytime / Noon and Evening / Weekdays and/or weekends and holidays
Special operation
operation form;
form; Time specific events
Environmental
Environmental conditions
onditions; Outside air temperature and humidity / Solar radiation / Sunny, cloudy, rain and/or snow / Seasons
M&E System operations
operations; Change the operation of the system for heating and cooling equipment / Changing the start and end
times of the equipment operation
・ Optimized depending on the building, minimizing energy consumption in all the
equipment and systems (Air-conditioning equipment, heat source equipment, such as
natural ventilation) that can be controlled.
・ During the building operation, the present algorithm is used to compare the predicted
demand and demand theory inhibition is calculated, along with the actual energy demand.
・ Perform a "change of control parameters" and "control pattern selection" according to the
monitoring data.
・ When there is a sudden change in the external environment, or configuration changes
that occur when a special event occurs, the present algorithm can revise the
demand pattern.
・ As a result, continue to run energy consumption and optimize energy efficiency.
8.0 Conclusion
Summary of research and development results.
Outcome 1
I revealed the basic framework of the real-time
monitoring.
Verification and basic functions and operations
of the prototype.
Study and technical elements of research and
development module structur
Apply the prototype to the
actual project
Outcome 2
Introduce the development research prototype
system to actual facility. Clarify the effects and issue.
・
Organize “ Visualization”
The use of monitoring data to determine the
waste of energy
Calculation and the amount of reduction of by
improving operation and settings.
・
Case study and application of data use
Do not you reduce the waste of energy by
the control equipment and systems?
By using real-time data, we try to introduce EMS to the
organization that owns the multiple facilities.
As the result, how to consider an integrated energy
management is possible.
We study how to organize the challenge to social
infrastructure.
・
・
・
・
Outcome 4
Optimization control by monitoring time-series data
Clarify the algorithm for optimized and high efficiency
energy use, and development system, and the scope
of application.
・
Optimizing control algorithm
・
Predictive control algorithm
・
AI control algorithm
Outcome 3
I have proposed an architecture that can be implemented in
order to be used effectively as social infrastructure. To clarify
the applicable conditions
Organize the following for integrated energy management.
Methods and integrated energy management by using
"visualization"
To organize its scope and issues
Differences in architectural applications
Difference by the organization
・
Requirements that make it difficult to introduce
・・
For the building of energy management is
absent / Can we management through 24 hours
a day, 365 days a year?
If you find a waste, Could we determine whether
the relationship between the improvement and
analysis results?
Outcome 5
By the results of the data analysis, found methodology to
determine the relationship between the solution and the
waste.In addition, we show the algorithm to determine the
relationship between, was clarified flow of data usage.
Outcome 6
By using real-time data monitoring, clarify the solution flow for implementing the optimal control and predictive control.
By introducing a system to manage the organization and its multiple facilities,
Clarify the scope of application.
In addition, I have also found that the flow solution for renovating the existing building, do the best combination of energy use
discovery, use of natural energy, energy conservation and optimal control. And I understand the scope and issue.
References
１）
２）
３）
４）
Hiroshi.SAKO, Bumpei.MAGORI, Tomonari.YASHIRO, Hayato.FUJII, Heuristic energy use optimization methodology by energy
use monitoring system Case study on large-scale complex building operation , Helsinki World Sustainable Building Conference, 102010
Hiroshi.SAKO, Bumpei.MAGORI, Tomonari.YASHIRO, Hayato.FUJII, Yoshikazu.MIZUTANI, Approach to the Social mechanism
to reduce the CO2 emission from existing buildings, Helsinki World Sustainable Building Conference, 10-2010
Tomonari.YASHIRO, Bumpei.MAGORI, Hiroshi.SAKO, Rozou OOKA, Hiroyuki SHIDA, Kyosuke HIYAMA, Zero Energy
Building project in the University of Tokyo, Helsinki World Sustainable Building Conference, 10-2010
Hiroshi.SAKO, Bumpei.MAGORI, Tomonari.YASHIRO, Study on reduction of CO2 emission in the existing buildings（Result of
case study buildings in City of Yokohama）, RE2010, June.2010
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