Add to Basket. Book Description Springer, Condition: New. New Book. Shipped from UK. Established seller since Seller Inventory IQ More information about this seller Contact this seller. Language: English. Brand new Book. This book presents acollection of recent research works that highlight best practice solutions,case studies and practical advice on the implementation of sustainableconstruction techniques. It includes a set of new developments in the field ofbuilding performance simulation, building sustainability assessment,sustainable management, asset and maintenance management and service-life prediction.
Accordingly, the book will appeal to a broad readership of professionals,scientists, students, practitioners, lecturers and other interested parties.
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Seller Inventory LHB Delivered from our UK warehouse in 4 to 14 business days. Never used! This item is printed on demand. Seller Inventory Book Description Springer Verlag, Singapore , Brand new book, sourced directly from publisher. Dispatch time is working days from our warehouse. Figure Connection schema Simulation Based Control This functionality of Energy IN TIME is given by two types of controls: one aimed on the generation of the optimal operational plan in advance taking into account weather and occupation forecasts Operational Plan Generator and a second one, Model on Demand Control that coordinates in real time the relevant equipment set-points by seeking the HVAC optimal behaviour.
This last control module is supported by two additional tools: Fault detection and diagnosis and Fault adaptive control. Operational Plan Generator, OPG The intelligent control system, developed in Energy IN TIME, for whole-building energy optimization adjusts HVAC set points continuously to be adapted to occupancy and weather loads and their predictions, minimizing HVAC energy consumption and peak demand while maintaining the indoor environment within user preferred comfort conditions. This results in the generation of the operational plan in advance to be implemented in the existing energetic systems in the building.
The procedure for the generation of operational plans uses information about: a the current building status i. The Operational Plan OP is a list of timestamped setpoint values which describe the actions to be performed on the building equipment at each interval. The optimized strategies provided by the OPG have been evaluated in order to analyze whether this system is able to provide better cost-effective functionalities to the current facilities in buildings. Control strategies and algorithms are responsible for maintaining a controlled variable to operate within acceptable ranges and to fulfill the desired functions.
The strategies have been defined aiming the energy consumption optimization. This option has the limitation of availability, since not all the locations have the same exploitation potential. The optimization of timetables would reduce the pumping consumption and the wasted energy as well, making the installation to operate just when it is necessary and, at the same time, maintaining the internal comfort and satisfaction of the people inside the building. A better use of efficient technics such as the freecooling and the heat recovery reduces the energy consumption of the central production satisfying the comfort and energy requirements from the final users.
After the analysis of the different options for defining energy efficient strategies, individual evaluation was done in each particular demo building in order to finally define which strategies would be followed and applied. The IPMVP is not a standard and thus there is no formal compliance mechanism, however it is based on calculate savings following a procedure. Particularly, savings are determined by comparing measured use or demand before and after implementation of a program in this case OPG , making suitable adjustments for changes in conditions.
Figure Energy savings evaluation. In order to evaluate a more fruitful period after the demonstration phase, a yearly analysis has been carried out, taking into account both the demonstration results and the information obtained in the simulation phase. Furthermore, since most of the demo buildings ICPE, Sanomatalo and Faro have been evaluated in one part the building, a projection to the whole building has been performed.
Results of the analysis in each demo building are described below: ICPE Office Building: This demo was the first to be operated with the OPG since winter in Romania ended at the beginning of April and the district heating was going to be cut off in consequence. Unfortunately, winter season was ending and the OPG application could not show a great potential, nevertheless, some savings were achieved.
In this case, occupancy was not taken into account since there was not any variation. The equation is the following: Figure The comparison of the adjusted baseline in red colour and the real measurements gathered by the monitoring system in blue colour is represented in the next figure: Figure ICPE comparison.
ICPE savings. The total amount of thermal energy savings achieved in the demonstration period were The running time for cold season Nov — Apr was hours. ICPE old pump. ICPE new pump. ICPE heating savings. The distribution of thermal savings hypothetically achieved in thanks to the OPG installation for optimizing the consumption in the whole building is shown in the next figure: Figure ICPE thermal energy savings profile.
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The optimization potential in this building is higher in the colder months where it is possible to adjust better the comfort temperature in the building as well as the water supply temperature from the district heating network. Thanks to the improvements done within the project, a more accurate set points have been instituted for an energy optimization of the heat consumption. Therefore, the linear regression was multiple and its representation is shown in the following image: Figure Sanomatalo multiple linear regression.
It can be observed that, according to the graph, there are some combinations where there is no energy requirement. This information would have been very useful for its utilization in the control strategies in that period before the implementation of Energy IN TIME measures. The demonstration activity started in April and was extended until the end of May The comparison of the heating consumption in the pilot area during the demonstration is shown in the next figure: Figure Sanomatalo comparison.
Sanomatalo daily energy savings. However, it is important to point out that this percentage of energy savings does not correspond only to the application of the OPG system but a combination of different energy efficiency measures particularly MODC and OPG modules. Sanomatalo — Projection study It is shown the District Heating consumption in for the pilot area and the whole building, the energy prices and the average energy savings obtained for the different season periods. Table 6. Sanomatalo energy savings. Sanomatalo thermal energy savings. The difference in this demo building relies on the optimization focus which is not only distribution and terminal units but also the generation system.
Acting at the beginning of the installation, it is possible to obtain much higher energy savings. The baseline period was studied with data from and the demonstration activity was carried out during the month of May Levi multiple linear regression. From the graph, it can be observed that, even with comfortable weather outside, there is some thermal energy consumption due to the DHW domestic hot water.
Levi comparison. Levi daily energy savings. Therefore, to give an optimized plan for this part of the hotel is not possible because the comfort temperature can be different from one person to other and, as a consequence, the thermal consumption of the zone will depend on the predilection of the people in each moment. The other zone analyzed is different because in this case it is mostly a common zone.
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Therefore, the thermal comfort can be fixed externally by the maintenance of the hotel or the OPG. The lineal regression obtained with data from is the following: Figure Levi 7th floor regression line. And the comparison between the adjusted baseline and the thermal consumption measured is shown in the next image: Figure Levi savings.
There are some days where was not possible to improve the thermal consumption; however it corresponds mainly to the first days of the demonstration period it could be because the maintenance people in the hotel did not perform all the recommendations that the OPG sent. It is shown the District Heating consumption in for the whole building and the estimated for the 7th floor, the energy prices and the average energy savings obtained for the different season periods.
Table 9. Levi 7th floor heating savings. In this case, the distribution of such energy savings in common areas would be the following: Figure Levi thermal energy savings profile. For the analysis of the results, August information has been used for developing the baseline and August measurements have been evaluated. One relevant point in this building is that cooling system is undersized for giving the maximum power that the cooling requirements need. This fact means that in the warmer period such as August the equipment is almost operating at its maximum power full time at least, it was the typical situation all the summers in Faro.
The OPG improves a little bit the energy consumption by reducing the number of operation hours some night when the flights were not so frequent and there was the possibility to optimize it. However, since the cooling requirements were so high, the energy savings in August do not represent the overall potential of the OPG system in Faro.
The lineal regression obtained in the baseline period analyzed August represents the fact explained previously, where the cooling system is operating almost all the time: Figure Faro regression line. The comparison of the adjusted baseline with the weather conditions in the Summer demonstration period is shown in the next figure: Figure Faro baseline comparison. Faro daily energy savings As it has been observed, it has been possible to optimize the operation of the AHUs in the rooms selected for the pilot area.
A new study was done with the evaluation of the electricity consumed. The regression line in this case represents the information of data monitored in before the demonstration phase. Faro electrical regression line. The comparion between the adjusted baseline and the monitored data in the demonstration is shown: Figure Faro baseline. Faro — Projection study It is shown the electricity consumption in divided in different uses the same proportion has been used for the estimation of the energy consumption in the pilot area , the energy prices and the average energy savings obtained for the different season periods.
Faro savings. The distribution of the electricity savings hypothetically achieved in thanks to the OPG installation for optimizing the consumption in the whole building is shown in the next figure: Figure Cooling energy savings profile. It has been observed that the warmest months in summer July and August do not represent the most energy savings but the lighter summer months June and September.
This is a consequence of the size of the installation because it does not offer many possibilities to propose different operational plans. In warmer period, the installation is working as it maximum power a lot of hours in the day, therefore, the optimization potential is not as high as in less warm periods. The overall goal is to minimize HVAC energy consumption while maintaining user preferred comfort conditions. In doing this, MODC showed the effectiveness of its adaptive strategy and the on-demand computation of low-complexity models, irrespectively of the HVAC architecture within the buildings.
Carrier Aquasmart integrated solution is a hydronic system composed of four type of equipment monitored and efficiently controlled by a System Manager. Aquasmart is installed in one of the office buildings of Carrier facility at Montluel, France. In order to have comparable boundary conditions occupancy, thermal loads, and weather conditions , MODC was demonstrated at alternate days with the Baseline. The comfort results are shown in Figure Baseline and MODC are totally comparable and they both guarantee comfort within the comfort band of Figure User comfort obtained during demonstration period at Montluel building.
Figure Energy reduction obtained during demonstration period at Montluel building. These two networks comprise 54 zones inside the building in different locations and cover the major part of the building. By collecting temperature measurements from all the zones, the MODC is able to guarantee comfort conditions in whole the building. As such MODC does not focus on a selected area in the building, but supervises the temperature regulation at the building level.
Mechanical schematics of Heat Exchanger LS The main control objective is the minimization of energy consumption while maintaining the indoor environment within user preferred comfort conditions. This objective can be expressed as the realization of a number of partial objectives: i keeping SWTs in safe operating values, ii ensuring the RATs measurement to be in a given comfort band and, finally, iii minimizing the energy consumption related to the actuation of control inputs SWTs. In order to compute the optimal set-points that bring the building behavior to optimal operating conditions with respect to the economic criterion, MODC continuously adapts the building model derived from the system measurements.
This iterative and adaptive behavior is implemented following the subsequent steps: i data from the system are acquired and analyzed in order to detect different operating conditions of the system, ii an on-demand low-complexity model of the controlled system is generated and iii used for re-tuning the parameters of the controller, which at the end iv selects the actual values of the SWT set-points. A long demonstration session from March 1st to April 29th was performed, during which MODC operated in different hours of the day, with a different heat demand and different outside conditions.
During this time span, MODC has been alternated with the Baseline controller in order to obtain a fair comparison of the two control strategies. The distributions of the RAT measurements acquired during the demonstration period are almost identical either when the building zones temperatures are controlled by the Baseline dashed red line or by the MODC continuous blue line. The fact that the Baseline and the MODC are comparable, from the point of view of the zones temperatures and achieved comfort, allows us to fairly compare the energy savings obtained by the MODC with respect to the Baseline.
RAT distribution. For a detailed analysis of the energy consumption, it was selected two different periods characterized by high and low heating demand from the building and with very close weather conditions. Average energy savings during high demand, low demand and the whole demonstration period. Conclusions of the results achieved after the demonstration period Carrying out the analysis of the outputs obtained in each demo building, some conclusions have been reached regarding the operation of the Energy IN TIME system.
In general terms, in an automatic scenario, the Energy IN TIME system reduces the energy consumption, optimizing the functioning of the equipment and maintaining comfort levels which have been always a limiting condition for deciding between plans or others. However, if the plans generated are used as decision support tool, the potential of optimization depends on the user responsible of following the plan suggested by the OPG. After the study, a less potential scenario for the OPG application has been detected, that one in which the comfort observation depends directly on the clients and they are able to interact with the equipment.
When there are extra manipulators involved in the operation of the system such as the clients in a hotel , the potential of the OPG decreases since the plans generated are based on a particular comfort condition usually limited by the regulations. On the other side, since the accuracy of the generated plans depends mainly on the weather conditions, a reliable weather forecast should be used. Within the project a study was done varying the external temperature in order to evaluate when the plan generated could not be suitable.
Furthermore, the OPG potential is higher with intermediate weathers, when there are more possibilities of optimization, therefore again, a confident weather forecast is crucial. Apart from the weather forecast system, it is a must to work with a precise calibrated simulation model. Therefore, Energy IN TIME prototypes have been demonstrated in different operational environment 4 buildings , obtaining always positive values.
In order to have a more representative period, a projection study has been done extending the results to yearly period and for the entire demo buildings. The particular case of Levi Hotel studied only one minor part of the building the common areas since it has been concluded that the rooms zone is not able to be optimized by an external energy manager due to the interaction of the clients.
Environmental aspects For the environmental analysis, CO2 savings have been evaluated after the implementation of the energy efficiency measures in the project. Environmental savings. FAC was demonstrated following the Model-In-the-Loop MiL approach, which allows validating equipment level faults and evaluating the impact of the FAC strategies by using complex models of the thermodynamics of the equipment. In particular, the valve stuck fault is proposed as typical example of faults at FCU level and it is validated in MiL simulation. Figure 39 shows the results of simulating a stuck valve fault on a typical cold day, where the outside ambient temperature changes from 0C to a peak of 4C at midday.
At time a fault occurs and the valve is stuck. As regards comfort one can note that the fault leads the hot air recirculate into the room hence causing persistent overheating. Therefore, without adaptation, comfort can be never met. Clearly to reject the fault an adaptation is required: the only possible adaptation is at the fan speed. Indeed due to the fault the fixed value of the fan speed is no longer valid and hence it should be adapted. In Figure 39 we can note that by adapting the fan speed the overheating in the zone is avoided and comfort can be met.
MiL simulation of the stuck valve fault. Consequently reducing the amount of energy wasted by building Heating, Ventilation and Air Conditioning HVAC systems can achieve much of the aforementioned energy saving objectives. Furthermore, the FDD would facilitate a significant value addition to the end-user in terms of efficient energy management and optimal equipment maintenance. Finally, the FDD module can provide information regarding existing faults to fault adaptive control modules to prevent further degradation of equipment and KPIs.
In addition to the real time monitoring technology of the building HVAC system, a customized on-demand building health analysis framework was developed for FCUs which can be used: a during the commissioning of an HVAC system or b for building health analysis on demand without installing the aforementioned full-scale real-time FDD system. The developed FDD methodologies were demonstrated in the Montluel.
A detailed analysis of the detected faults using this real-time monitoring methodology was performed with 60 FCUs due to availability of data not all FCUs were used , the detailed results are shown below in Figure The results were physically verified for assessment of performance.
Detected Faults and Corresponding Physical Verification. On demand building health analysis demonstration results are shown in Figure Maintenance Techniques and Continuous Commissioning Predictive maintenance Predictive Maintenance PM module implementation is demonstrated in Levi site according to the availability of degradation and failure data. The Predictive Maintenance module monitors the physical condition of the equipment in order to carry out the appropriate maintenance works to maximize the life cycle of HVAC system without increasing the risk of failure, guaranteeing comfort and reduce energy consumption.
Predictive Maintenance is working closely with Fault Detection Diagnostic, which provides measured indicators of degradation and the list of faulty and degraded components by detecting slow or abrupt jumps in these indicators. Predictive maintenance provides a schedule of future maintenance activities, and gives information to the Decision Support Tool. PM consists in 2 processes: prognostic and maintenance decision making see Figure 39 Figure MO11 Architecture The prognostic process embeds a degradation model of the equipment.
This degradation model is mainly constructed based on historical data. When using it, it is updated depending on stress, age and degradation current measure. Degradation model is projected over future in order to predict remaining useful life of the item in consideration. Maintenance decision consists in planning maintenance actions at the right time in a dynamic way, i. This module provides an optimal maintenance date to maintenance manager. Optimality refers to reliability if the equipment, in relation to the prognostic, availability of the maintenance crew and HVAC system and maintenance action duration.
The methods proposed and validated to detect valve faults valve blocked at different opening could be also used to detect leakages in the valve. Actually the proposed approach relies on the estimation of the valve opening, using redundant information. The fault is detected when there is a deviation between the valve opening command and the valve opening estimation. A valve leakage will also induce a discrepancy between the valve model and the actual measurements, therefore making it possible to be detected.
Continuous commissioning Basic idea of the continuous commissioning is to have continuous monitoring for some building equipment or HVAC system and detect indicators for high energy use, unexplainable increase in energy consumption, constant failure of building equipment or system and continuous occupant complaints about indoor temperature, air flow and indoor air quality. With the continuous commissioning, the target is to see errors in advance and find the reason why systems are not working as designed. With continuous commissioning it is expected to have savings for energy consumption and maintenance actions and also increase the quality of the indoor air.
In Sanomatalo there has been problems with the VAV-dampers; they often are stuck in one position and that is noticed only when there comes complaints or the energy consumption has been increasing. On the building management side the complaints are usually signs from the unhappy tenants, and they try to avoid them as much as possible. Rise of the energy consumption is often detected late, which depends on the activity of the building maintenance crew. There might even be situations that the consumption has been raising and that has not been detected. In the building like Sanomatalo, one stuck VAV unit cannot be seen from the energy readings.
Usually in VAV-systems, there are two typical faults that occur; mechanical jamming of the VAV-damper and a measurement error in the pressure difference sensor in the VAV-damper. After the implementation, the BAS can monitor air flows in the VAV-dampers and compare them to the designed air flows, and detect malfunctioning dampers. Also the total measured air flow in the space can be compared to the indicated air flow of the air handling unit. This will indicate if there are faults with the air flow measurements.
In addition, the system runs a testing procedure once a week to ensure all dampers work as intended and have no mechanical problems. As a result of applying the continuous commissioning methodology, indoor climate in the targeted area is more controlled and unnecessary use of energy is minimized. Since operating, the system has been alarmed total 11 times. Most of these 10 alarms are from the supply sides upper limit. That means that during the time alarm occurred, there was going too much air than designed in to the spaces. Those would indicate that the damper had some jamming or slowness when the damper should be closing.
The alarm log does not show when was the alarms happened, only when was the latest alarm and how many alarms in certain data point. Also the comments from the maintenance people were that these VAV units did not cause any maintenance at all. Without the continuous commissioning alarm system, and more precisely without the weekly testing procedure, all of those alarms could have caused extra maintenance. History shows that in Sanomatalo the VAV units are key part of the maintenance costs.
Normally it is possible that the VAV unit is in the same position quite a long time which causes jamming in the long run. Also important advantage of this system is that the system will inform from the possible failure, not the user in the spaces.
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In that way the user satisfaction stays high. Decision support tool for building design and retrofitting The prototype of decision support tool has been developed to help facility managers in designing and assessing mid-long term building energy renovation scenario for the Faro airport pilot site. The methodology used in the tool includes: a a dynamic energy simulation model COMETh , b a Life-Cycle Analysis method which reads Energy Product Declaration of construction products, and c a Life-Cycle Costing algorithm, and d a module dedicated to scenario ranking. These 4 models and methods exploit inputs extracted from a BIM file quantities and metrics.
This decision support tool has been tested to the terminal building of the FARO airport. This allows not only for a remote management but for the possibility to manage many different facilities from a unique location, what goes in favor of scalability, for example from the point of view of an energy service provider as stakeholder for Energy IN TIME solution. This feature makes possible to deal with a large number of buildings at the same time and operate the solution implemented in them with a common management.
The web application implemented for the CRC provides a common interface to all the modules. A user-friendly interface makes easier the interaction with the modules because it avoids the complexity of using many different interfaces implemented by different designers and with different technologies. The CRC unifies appearance through a unique accessible technology. The interface provided by the web application is mainly devoted to monitoring activities.
Main activity is not only to monitor the equipment or facilities in general, what is a task typically implemented by local utilities and SCADA software associated to the BMS of the building. All these modules are present in the CRC web tool through their implementation for the different 4 demonstration buildings not all technologies are present in all buildings and the pilot scale laboratory that were selected for the project.
High level operation of the control set points can be deployed automatically or under the supervision of the building managers. In the scope of the Centralised Remote Control these approaches have been implemented as a non-graphical automated application that will be used only for fully automatic control and a graphical interface for the visualisation of the operation plans that allows for supervision and tracking for manual implementation of the control actions. The Set-point Provider Tool, based on the same connectivity and interactive features used by the web tool, complements its functionalities with remote abilities for control set-points managing in time and other advanced monitoring features.
Building owner conclusions: Energy IN TIME modules demonstration revealed expected benefits like energy savings but also provided new ways to monitor HVAC functioning from the point of view of energy performance. The demonstration of the control tools showed real results and also increased awareness of the building users. The maintenance block generated useful information how to detect possible faults in the systems and the development continues after the project. Faro Airport Faro Airport has several different systems that monitor and control all the electrical, mechanical and electromechanical equipment.
Having a Centralized Remote Control tool that can interact with all these systems, analyze all the outputs produced and, without human interference, implement all the necessary changes to improve the air quality conditions and, at the same time, increase energy savings is a great advantage to any company that deals with lots of different systems and variables. Energy IN TIME project is an added value feature for Faro Airport maintenance team, as they now spend less time monitoring and acting over the equipment, which improved their efficiency in other tasks.
However, Energy IN TIME installation was not easy, as Faro Airport terminal is a very complex building and, mainly, because in October of a contract started to extend and refurbish nearly all the terminal. As Energy IN TIME started 2 years before with the objective of being implemented in all the building, Faro Airport had to reduce the scope area of the project to an area that would not be changed by the refurbishment works.
This was only an area reduction and not a design feature reduction. Faro Airport team is very interested in keeping and improving this solution in their building and they will certainly try to expand it to the rest of the building and add some more features — the lighting, for example.
Since the system was implemented the comfort level increased for all occupants of the building and energy savings were achieved during the testing period. Without Energy IN TIME the control was done in a semi-automatic way based on the energy manager of the building, thus meaning that the system was not very efficient.
Now with the implementation of Energy IN TIME solution — the OPG module — the control is done an automatic close loop that has different feedback from the building to adapt and react to changes during the day. This is why further implementation into other non-building will be done more easily into new buildings then old ones, but it is possible for old buildings also. The feedback received during the 2nd user comfort questionnaires phase was positive and based on the results the majority of the building users sad that the comfort has improved in the ICPE Office Building and in the pilot areas.
For our experience in ICPE demo the Energy IN TIME solution implemented provided a series of improvements in the heating system operation with can be translated into greater comfort for all building users and improved energy efficiency. Sanomatalo and Levi All of the modules that has been demonstrated continuous commissioning, predictive maintenance, model on demand control, optimal operation plan generator has given valuable information of the new possible ways to improve HVAC operation. There has been clear energy savings for demo buildings and new ways to perform maintenance actions and detect maintenance need.
All of the modules have given new business model opportunities for Caverion. No need to program the actual BAS. The existing heating curve is automatic. To get the most optimal set points, manual work must to be done. The Predictive maintenance was more helpful and in the future would give more data to realize potential faults in the HVAC systems.
The maintenance actions would be known in advance and unnecessary actions would be avoided. So there was no increased work. Meanwhile it gives energy savings which will help on the energy management. Continuous commissioning gives main advantage because of the automatic monitor. Before there had to be done physical checks and now it can be done automatically by the Continuous commissioning. This improves maintenance efficiency by decreasing the unnecessary faults. EiT gives added value for the buildings. Also in Levi the Predictive maintenance has shown potential to improve the current maintenance actions.
As demonstration results have shown, there has been detected clear energy savings. Main problem trough out the project was the jamming of the BMS which created some difficulties. But it did not sabotage these results. In Levi the non-automatic control already increased the potential to see energy savings. Also in Levi the geographical location was slightly challenging, which slowed down some development process. Main added value for demo buildings have been the demonstration results and for Caverion the new business models. Also important part is that these new features can be done by just collecting the data from BAS and processing it somewhere else.
Sanomatalo has shown clear interest for the demonstrated modules. Especially the Continuous commissioning has interest them, and after showing the demonstrated results, also the control tools and their energy savings have started promising talks. On the other hand in Levi the OPG and manual control was not very interesting for the building owners. But this will not exclude the possibility to have automatic control tools which would raise interest.
Predictive maintenance is on development phase but the results have been promising. Especially for the site where the location is difficult, predictive maintenance practice for at least some of the HVAC components have been interesting idea. Idea to decrease sudden maintenance cost has been interested Levi. Also there has been done some works that has not been shown in the results. In Levi there was created a new way to control floor level dampers. Unfortunately this was not able to demonstrate because it needed actions from Levi hotel personnel. First idea was to connect that to hotel reservation system and control dampers automatically.
In Sanomatalo there was built an AHU air flow decreasing program which starts when the outside air temperature drops down. Both of these functions can be process to be part of continuous commissioning by constantly following needed values and control the systems based on them. Taking full advantage of Simulation as well as Model Based Control techniques, the project aims at developing an integrated approach to building operation, maintenance and management.
Additionally, the number of simulations needed to meet the optimal system efficiency will decrease as they will be run on the actual representation of the building calibrated simulation model , thus, reducing the uncertainties and consequently, the assessment time.
Moreover, additional benefits are expected from up-to-date building simulation models and the continuous analysis, by means of data mining techniques, of the information collected for pattern extraction. The combination of both techniques reduces the assessment time needed and will support a more effective decision making when accomplishing a building retrofitting or renovation process, as most of the information needed for the project design i.
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Commercial buildings comprise the largest portion of the non-residential stock while office buildings are the second biggest category. Figure Share of total energy use per building type Figure Non-residential building Stock Energy can make up to 19 percent of total expenditures for the typical office building, representing the single largest controllable operating expense for office buildings, typically a third of variable expenses.
Effective maintenance can reduce HVAC energy costs by 5 to 40 per cent depending on the system or equipment involved. Unless an adequate maintenance of HVAC components are not continuously perform energy performance degradation will occur. Regular scheduled maintenance of heating, ventilation and air conditioning HVAC systems can increase their efficiency.
Main dissemination activities To raise awareness on the innovative solutions developed within the Energy IN TIME project and facilitate their introduction onto the market, project partners have participated in a wide number of dissemination activities. Early on in the project, graphical material has been produced for the Energy IN TIME project to ensure consistency and efficiency in communication activities. As part of this material, a visual and written identity has been developed for the project together with key messages on technology, energy and economic impact.
In addition, a project leaflet has been created and updated to include the main project achievements.