Community Research and Development Information Service - CORDIS

FP7

ENERGY IN TIME Report Summary

Project ID: 608981
Funded under: FP7-NMP
Country: Spain

Periodic Report Summary 2 - ENERGY IN TIME (Simulation based control for Energy Efficiency building operation and maintenance)

Project Context and Objectives:
Energy IN TIME Context
Buildings Operational stage represents 80% of building’s life-cycle cost of which 50% is consequence of the energy use. Moreover, up to 90% of the buildings’ life cycle carbon emissions occur during their operational phase, mainly as consequence of the HVAC (Heating, Ventilation and Air Conditioning), lighting and appliances’ energy use . Therefore, energy and cost saving strategies addressing this building operation phase will have a major impact in the building life cycle cost.

Figure 1. Building Life-Cycle

The buildings’ energy demand and consumption is influenced by numerous factors both inherent and external to buildings. Aspects such as the constructive characteristics, climate, building usage or users’ behavior, among others, directly affect the final energy performance. Normally, these factors are strongly considered at the building design and planning stage. Buildings are usually designed to cope with the most unfavorable operating conditions (e.g. crowded room), resulting in an inefficient operation of the system and extra costs (e.g. oversized HVAC systems). Moreover, operational plans are usually based on fixed schedules, sometimes manually modified by operators, which do not use the knowledge of external conditions to take advantage of the design of more efficient plans.
Furthermore, it is important to highlight that a good energy design of the building does not implies to obtain a good energy performance of the building. The integration of adequate control and operational strategies is a must in order buildings reach to their optimal efficiency levels.
Energy IN TIME (EiT) project develops a Smart Energy Simulation Based Control method to reduce the energy consumption and the energy bill in the operational stage in non-residential buildings applying new techniques based on the prediction of indoor comfort conditions and user behaviour performance to improve the Lifetime and Efficiency of Energy Equipment and Installations through continuous commissioning and predictive maintenance.
Using Energy IN TIME solution will be ensured the optimal energy efficient control for the building that means:
• Energy will be used IN TIME, only when it is really needed
• Energy will be consumed, only what it is really needed
• And Energy will be used, in the most efficient way

Energy IN TIME operating principle
There are different abstraction levels, or layouts in Building Energy Control.
• Execution Level in which the daily operation stage of the building is controlled through the BEMS providing the set points for each equipment or system. A sensors network will detect faults occurred in actuators, systems or even in sensors, allowing for keeping the building working properly as it has been designed .
• Supervision & Reconfiguration Level where, supported by monitoring and fault Detection & Diagnosis, it will be managed the Simulation based Control. At this level, using simulation techniques, the actual energy demand monitored is compared with the simulated one in order to control the existing gap between both of them. Whenever the gap is higher than the desired one Energy IN TIME looks for the cause, solve it and, if necessary, reconfigure the building energy system setting new set points adapted to the new situation.
• Operational Plan Generation Level. At this level the Optimal Operational Plan for the particular building configuration is generated according to the external variables such as weather, user behavior and energy prices.... By using forecasted data this Plan is generated in an anticipated way and it will be launched to the building IN TIME and operated by the energy system installed. For the Optimal Operational Plan definition Energy IN TIME applies State of the Art simulation techniques and optimization algorithms that ensure the minimum energy consumption maintaining the comfort conditions specified by the users.
Enabling more robustness to the solution, Energy IN TIME is complemented with two additional tools:
• Decision Support Tool, at a Decision Making level, gathering all the data related to the building operation and status and using this information Energy IN TIME offers an additional tool very useful for refurbishment, retrofitting works as well as for new buildings design.
• Trying to cover the maintenance operations, Energy IN TIME develops a Continuous Commissioning methodology focused on keeping the equipment at its most efficient state being reinforced by a Predictive Maintenance module in the Global Energy IN TIME solution.

Figure 2: Energy IN TIME Control Levels

Energy IN TIME Solution will be managed in a remote way, centralizing all the modules in a Central Remote Control and being automatized. This issue makes the Energy IN TIME allocation independent to the building, being possible to allocate it in servers outside the building as could be the case of a server allocation in an external energy service company (ESCO) facilities.

Main Characteristics of Energy IN TIME solution

Centralized Remote Control building platform
The most usual/common way to handle BMS in buildings is by Facility Services Companies’ maintenance operators focused on maintaining the hydraulic, mechanical and electric system working correctly and assuring the comfort conditions according to the user’s demand but they do not concern for energy efficiency or for reducing the energy consumption.
By contrast, the tendencies towards an energy efficiency growth in buildings go in the direction on offering remote energy efficiency services to the users consisting in offering energy auditory, monitoring and generating energy consumption reports. But not managing and controlling the BMS in order to achieve an energy efficiency improvement.
Energy IN TIME offers a centralized control building platform able to communicate with Building Management Systems devices giving them the optimal operational plan according to the particular building configuration and taking into account external variables related to weather conditions, users’ behavior, building usage schedule... and achieving a higher energy efficiency level in the building which will be traduced in important energy savings in consumption.

Simulation based Control.
Using building energy model and simulation techniques we are able to define the building energetic behavior in different scenarios. If we add the knowledge of forecasted weather conditions and building usage stage it is possible to calculate the building energy demand in an anticipated way for a particular period of time.
Knowing the building energy demand in advance it is possible to anticipate the energetic building configuration necessary for matching this demand.
Energy IN TIME calculates the optimal operational plan in order to match the expected demand, anticipating the building energy production according to this demand.

Data repository
All data gathered from sensors or measures referred to indoor and external parameters are stored and used for additional tools for predictive maintenance, Continuous Commissioning and Decision support tool.

Energy IN TIME methodology

The Energy IN TIME installation complexity is dependent on the existing control systems in the building. The more advance systems installed in the building, the simpler is the Energy IN TIME implementation and lower are the initial inversion costs.

1. Building characterization. Energy Review.
As a first step it has to be characterized the building by the identification of all its architectural elements (materials, U-values, envelopes, windows distribution, etc...) and by the identification of all the energetic systems installed in the building that provide energy to satisfy the energy demand.

2. Analysis of the existing Building Energy Control Systems.
It is necessary to analyze the existing Building Energy Management Systems (BEMS) not only at the component level as well as programming level.
Sensors and actuators available in the building have to be analyzed in order to detect additional needs for the correct Energy IN TIME functionality. The communications elements/specifications with the BEMS and all the measurement´s systems installed in the building and relevant for an efficient building energetic control will be also a part of this analysis in order to establish a correct communication flow between Energy IN TIME solution and the Building and its systems.
At a programming level, the control strategies are implemented in the BEMS identifying the benchmarking and all the susceptible actuations to be controlled by Energy IN TIME.
3. Identification of the writing variables to be controlled by Energy IN TIME
This step is focused on the writing variables identification. Being these variables related to the energetic system, included in the BEMS program and relevant for the building energetic control allowing their modification from Energy IN TIME. These variables are mainly references to equipment automated control by direct activations/deactivation or by time schedule.
4. Installation of Gateways for Communication with the Energy IN TIME Platform
It is essential to establish a reliable and robust communication system between the Energy IN TIME and the existing BMES in order to communicate the optimal operational plan for each selected period of time determined previously by Energy IN TIME solution.
5. Original control program adaptation for the Energy IN TIME Platform housing
Once the existing Energy control system in the building has been analyzed, the strategies identified and the control variables defined it will be necessary to modify the original program in order to allow the strategy implementation in a remote way from the Energy IN TIME Platform.
This adaptation will be done maintaining the original structure and operational mode, offering the possibility to turn back to the local building control instead of the remote Energy IN TIME control, with the original Automatic/Manual structures as originally established.
6. Energy IN TIME programing
The Energy IN TIME solution will be programmed in order to calculate the optimal operational plan for the building particular configuration.
The optimal operational plan will be determined using energy modeling for knowing the energetic behavior in the building. The additional information regarding weather forecast, uses and schedule prediction will allow determining, in an anticipated way, all the feasible operational plans in order to reach the predicted demand.
Attending minimal costs in production, maintaining the predicted demand, criteria Energy IN TIME determines the optimal operational plan and lunches it to the BEMS in order to be implemented.
7. Put into operation the BEMS controlled by Energy IN TIME
As a next step, once the Energy IN TIME is configured and connected to the existing BEMS, it is necessary to set the relevant tests and performance monitoring to ensure the reliability and robustness system
8. Daily optimal operational plan implementation in local mode
As a previous phase to the global system operation, it will be set a period where the operational plan will be launched in the BEMS by the operation team in a local mode
9. Automated operation mode under supervision
After the Energy IN TIME solution is tested at a local mode, the system will be tested remotely as an automated operation mode under supervision in order to prove the reliability of the Energy IN TIME solution
10. Building operation by Energy IN TIME
Once the test phases have been concluded and the reliability and robustness of the system has been proven, the building will be operated by the Remote control Energy IN TIME system.

Use cases
The project will be validated on a number of existing buildings where the service will be provided. These buildings will be in different Europe locations with different climates. The concept will be tested in four buildings with different typologies and building use: An Airport, Offices and Test Lab, Commercial and Office, and a Hotel. By doing so it is ensured that the validation is made with different combinations of typologies, climate conditions and user behaviour. The demo buildings are listed below:

• Airport in Faro (Portugal): one of the eight airports managed by ANA. It was built in 1989, with the last refurbishment performed in 2001, and has a surface of 41000 m2 of built area. It is characterized by open spaces with big flows of people at certain times of the day, in correspondence to flight arrivals or departures. Its main energy source is electricity. High level operational plans must be implemented, aimed at maintaining pre-defined environmental conditions.

Figure 3. Faro Airport

• Offices and Test Labs in Bucharest (Romania): it is property of ICPE, built in 1982 on an area of 17384 m2. It has closed and distributed spaces with constant flow of people and scheduled occupancy. There is a strong presence of solar energy (both thermal and photovoltaic), and district heating is the base system for heating, covering 90% of the needs of the building. Fixed-schedule operational plans for indoor conditions are the only controls at the moment.

Figure 4. ICPE’s offices in Bucharest

• Commercial and office in Helsinki (Finland): managed by CAVERION, it was built in 1999 on an area of 38190 m2. On the first floor there is a public area with commercial and restaurant usage, the rest are offices: there are open spaces and distributed spaces with varied flows of people and scheduled occupancy. The main heating source is district heating (3.000kW). Chilled beam cooling panel is used for the main cooling and double facade helps to reduce over heating in summer. Zonally fixed-schedule operational plans for indoor conditions are implemented.

Figure 5. Commercial and offices in Helsinki

• Hotel in Levi (Lapland, Finland). This building is of very recent construction (2010), and has a total built area of 42500 m2. The structure is subdivided into an hotel (170 rooms),a parking, 2 apartment buildings with seasonal and high variable occupation. The main heating source is district heating (3.630 kWh), with radiant floor installation with a heating power of 660 kW. The four different sections have four main heating distribution centres with different usages.

Figure 6. Hotel in Levi, Finland

Expected results
The main output of the project is the creation of a control tool for the building energy management systems, which will be automatically and remotely operated. It will allow reducing the energy consumption of the building by optimizing the use of the resources: its strength lies into the fact that the building is monitored continuously and controlled according to the inputs received by the system.

The tool has a complex structure, being it made of many parts which are developed separately but interact with each other for the correct functioning of the EiT tool. Each of those parts can be exploited on its own, and can be summarized as it follows:

• Simulation Based Control: buildings application. The project’s innovative aspect is related to the integration of predictive methods for the periodical design and actualization of operational plans and strategies with adaptive algorithms for real-time control of the deviation in energy system performance focused on building energy management. The use of these control methods to operate existing building energy systems will result on 20% saving over previous energy consumption.

• Dynamic simulation building models for building control use. The outputs are the development of more advanced calibration techniques, creation of automated calibrated models integrating the energy audit procedure, continuous calibration.

• Fault Detection and Diagnosis and Predictive Maintenance. An automated fault-adaptive control platform will be developed in EiT which will integrate Fault Detection and Diagnosis and Simulation Based control algorithms by introducing a reconfigurable control layer between them which, by using data from the simulation models and real measurements, will reconfigure the controller to meet the required energy performance and comfort measures.

• Maintenance techniques. The result will be the creation of predictive maintenance concepts within the building day to day operation. Thanks to the information obtained from the simulations done with the calibrated models of the building and the actual information monitored from the building, it will be possible to track the health degradation and to predict failure progression.

• Data acquisition methodology & Communications Platform. EiT project will built upon an existing communication platform: the result will be a scalable platform for time-critical processing of large amounts of data in the cloud for critical infrastructures deployed in environments such as Airports. This will be supported by the replacement of expensive physical sensors with virtual sensors.

• Predictive models for user behaviour. EiT project will focus on understanding users’ interaction, extracting patterns of their behaviour, and analysing their effect on required energy for specific periods of time. This capability will result into the anticipation to room/building usage, by enabling predictions and more reliable information about requested energy consumption.

• Decision support tools for building design and retrofitting. This methodology will be integrated as part of a recently developed prototype decision support tool: it aims at sorting out the appropriate energy decision at level of the building envelope and the building equipment.

Project Results:
During this second period all the individual elements (modules) have been developed taking into account the peculiarities of the demos. System architecture has been updated taking into account the last interactions between all the modules involved in the project and all the necessary platform components have been created.
At the moment, all the demos are connected to the EiT Platform, each one with its own configuration. Data collected by the sensors and meters installed in the buildings are storage and are available for all the developed modules within the project.
Simulation Reference models (cloud based) have been completed for all the demos as well as their calibration elements for updating the static model parameters that will close the gap between estimated energy consumption and real-life energy consumption providing accurate simulation reference models for the EiT demo-sites to be used for the OPG module
For the integration and interaction between the control modules (OPG, MODC and FAC) it has been implemented, as a temporal database, the blackboard as mean to achieve this purpose.
Additionally, required data bases have been implemented aimed to give support to OPG allowing easy adaptation to different buildings and system configurations.
The control strategies for all the demo sites have been analised and defined taking into account the current building situation, in reference to their control and communication requirements.
MODC, FDD and FAC modules have been validated in the laboratory scaled demo, sited in Montuel, where have been induced different equipment and system faults in a real mode.
A continuous commissioning methodology has been implemented in one of the demo building, Sanomatalo building. Regarding predictive maintenance, the main equipment present in the demo buildings: heat exchangers, pumps, valves, fan, dampers and filters have been targeted for a FMEA analysis in order to analised the impact on KPIs. Reliability and maintenance data for FARO building have been gathered and an algorithm has been developed for decision making in case of a retrofitting action and it has been validated in laboratory on the basis of computer simulation.
The BIM model for FARO have been finished during this period and integrated into the decision support tool prototype, also designed in this period, together with a data mining methodology.
The Centralized Remote Control tool has been designed in a preliminary version, in form of a web application, with the purpose of providing the building manager or any other stakeholder with the ability to monitor and manage the EiT system from different places and not on-site.
System integration activities for the EiT demonstration in the four building have been also carried and the first steps for the demonstration plan for each building have been done.
Particular progresses in each of the WPs are detailed in the core of the report for this period.
Progresses in this period put the EiT system ready for the demonstration phase that will start in the following period.

Potential Impact:
Expected results
The main output of the project is the creation of a control tool for the building energy management systems, which will be automatically and remotely operated. It will allow reducing the energy consumption of the building by optimizing the use of the resources: its strength lies into the fact that the building is monitored continuously and controlled according to the inputs received by the system.

The tool has a complex structure, being it made of many parts which are developed separately but interact with each other for the correct functioning of the EiT tool. Each of those parts can be exploited on its own, and can be summarized as it follows:

• Simulation Based Control: buildings application. The project’s innovative aspect is related to the integration of predictive methods for the periodical design and actualization of operational plans and strategies with adaptive algorithms for real-time control of the deviation in energy system performance focused on building energy management. The use of these control methods to operate existing building energy systems will result on 20% saving over previous energy consumption.

• Dynamic simulation building models for building control use. The outputs are the development of more advanced calibration techniques, creation of automated calibrated models integrating the energy audit procedure, continuous calibration.

• Fault Detection and Diagnosis and Predictive Maintenance. An automated fault-adaptive control platform will be developed in EiT which will integrate Fault Detection and Diagnosis and Simulation Based control algorithms by introducing a reconfigurable control layer between them which, by using data from the simulation models and real measurements, will reconfigure the controller to meet the required energy performance and comfort measures.

• Maintenance techniques. The result will be the creation of predictive maintenance concepts within the building day to day operation. Thanks to the information obtained from the simulations done with the calibrated models of the building and the actual information monitored from the building, it will be possible to track the health degradation and to predict failure progression.

• Data acquisition methodology & Communications Platform. EiT project will built upon an existing communication platform: the result will be a scalable platform for time-critical processing of large amounts of data in the cloud for critical infrastructures deployed in environments such as Airports. This will be supported by the replacement of expensive physical sensors with virtual sensors.

• Predictive models for user behaviour. EiT project will focus on understanding users’ interaction, extracting patterns of their behaviour, and analysing their effect on required energy for specific periods of time. This capability will result into the anticipation to room/building usage, by enabling predictions and more reliable information about requested energy consumption.

• Decision support tools for building design and retrofitting. This methodology will be integrated as part of a recently developed prototype decision support tool: it aims at sorting out the appropriate energy decision at level of the building envelope and the building equipment.

List of Websites:
www.energyintime.eu

Related information

Documents and Publications

Contact

Miguel Angel Paris Torres, (Head of financial & Project Management Area)
Tel.: +34917912020
Fax: +34917912101
E-mail
Record Number: 193562 / Last updated on: 2017-01-18