Question: Write a Thesis on given topic, "An Evaluation on Energy Performance Evaluation and Comparison: A Case Study of URS Building"
It has been found that there is a close relationship between energy as well as the indoor environmental performance of the building with the outdoor or indoor climate. It can be better judged with building characteristics or by the behaviour of occupants. There are building simulation tools which cannot be replicated precisely for the sake of actual performance of the buildings as the simulations are also depended on the number of some primary assumptions which will affect the results. Therefore, it is very obvious that the calculated energy performance can be differing more significantly as there is the real energy consumption. There is another key reason that the current inability for proper model occupant has the same behaviour as well as they are found to quantify some associated uncertainties for the sake of building performance predictions. In this study, there is an elaborate discussion on energy monitoring as well as the building occupancy or the people behaviour. The occupancy behaviour in case of buildings is now a very good topic of research for the concerned of building systems and this topic becomes more sophisticated when the people have spent more time inside the buildings. This, in turn, makes the occupancy behaviour one of the leading influential issues for the energy consumption within a building. This occupancy behaviour also shows some random, spontaneous or the purpose-driven, like entering/leaving a room or consuming the domestic hot water or using any appliances. This can be also related to comforts like adjusting the thermostat or opening any window for the purpose of fresh air or the overall ventilation along with some closing blinds. On the other hand, the indoor environment is totally depending on the temperature, humidity or the illuminance levels or the air quality etc. for which the total energy will be consumed within the building itself. This study thus evaluates the challenges for the energy monitoring within the building and also an emphasis on the building occupancy as well as the heat energy that are used. For this purpose, the quantitative data analysis has been drawn for analyzing the correlation and also the relation between the buildings occupancy as well as the used heat energy. It is also pointed out through the data analysis in case of no or less building occupancy, in which way the waste energy has been transformed or behaved inside the building. In this respect, several times have been chosen and the occupancy energy has been calculated based on those time zones in the weeks. The correlation principle has applied to evaluate various energy gaps between several time zones.
It has been observed that there is a steady rise of energy consumption of the residential as well as commercial buildings in very recent years. The high increase in energy consumption is due to HVAC systems. For the presence of expected energy loads along with the storage capacity and the transportation and also the behaviour or user has influenced the quantity as well as the quality for the energy consumed in a daily basis for the construction of buildings. It is, however, the technology which is thus available can also be monitored accurately, collect or store the huge amount of the data involved in this process. Moreover, there is the technology, which is now capable of analysis as also exploitation of many data in meaningful ways. Also, the usage of data science techniques has a great impact on the increment of energy efficiency and this becomes a great deal of the attention along with the interest. It is also been observed that HVAC systems have engaged in the consumption of approximately forty percent of total building energy consumption for maintaining the healthy as well as comfortable environment inside the buildings. On the other hand, this HVAC system has created the network with other subsystems and those exist as the heat transfer for balancing among the different zones of the building (World Health Organization , 2015). This also includes the gaining of heat energy along with the losses by the means of building’s envelope. Moreover, the diverse occupancy which is known as the diversity with the occupancy level of building in spaces and that result in the increment for leads which has the actual demand of the HVAC system. In this paper, thus the framework has been introduced through which the quantitative evaluation of the energy implications for the occupancy of the diversity of the building level has been discussed. The information modelling related to the building is thus integrated as it provides the building geometrics and also the HVAC system layouts and also the spatial information where the computing potential of energy implication has been elaborated. In this respect, the overall building energy simulations for the real world building along with the reference building has been demonstrated with the proposed framework. This framework is found to effectively quantify the HVAC system of energy efficiency which is thus affected by the occupancy diversity. It is also needed to consider that the framework can be generalized for different building with different geometric along with the layouts or the occupancy diversity (Yang & Gerber, 2016).
This study thus focusses on the findings of the renewable and sustainable energy resources for the means of construction of buildings and also the occupancy diversity in which the energy consumption is also found to be different in a different situation. The system correlation analysis is thus performed on the basis of performing to nurture the energy consumption in different time zones inside the buildings (Labeodan, et al., 2015).
Occupant behaviour has found to have an important impact on the basis of building energy which is used and also represents the most significant uncertainty as it affects the effectiveness of the building retrofits. According to Owens and Wilhite (1988), there is 10% to 20% of the energy coming from domestic environments for the usage of Nordic Counties which can be utilized only through the change of occupant behaviour. As per the investigation of householders ‘energy consumption behaviour described by Yohanis (2012), there are the significant energy savings and that can be improved the energy awareness of the occupant. Additionally, it has been observed that there is more demand for energy efficient buildings and for that the construction industry has to face some challenges and thus it is ensured that the energy performance has been predicted in the time of design stage after the completion of the buildings (Chen, et al., 2013). Through some extensive evidence, it can be thus suggested that the buildings cannot be predicted or performed better in long run. It is also seen that some buildings also fails to perform the designers intended in partly basis when the user is found not to operate the buildings properly or it can also happen there are some occupants who use to behave different manners that the expected scenarios of designers. Thus the differences of the real as well as the predicted energy thus can be utilized which is depending on the differences among the predicted as well as the actual realization in case of the construction together with the technical installations and also there is an impact on the real usage of this building system which is operated by occupants. In most recent case, it has also been shown that the behaviour of the occupant has played some fundamental role in the amount of energy usage inside the buildings (Azar & Menassa, 2016). As per the study by Santin, et al. (2009), it is very important to notice the characteristics of the householders and the occupant’s behaviour on behalf of the energy usage in the space or in water heating within the Netherlands. It also concludes the characteristics of the occupant and their behaviour has a significant effect on the building energy use. Also, according to Virote and Neves-Silva (2010), there is always an expected return from the proper utilization of energy-efficient technologies which can be weakened through the improper usage of occupant behaviour inside the building. For better prediction of the energy savings, they also have utilized the occupant behaviour models for the building performance simulation model which is based on the stochastic Markov chains theory. In this context, Li (2015) has examined the impact for the actual building occupancy for the sake of assessment of energy conservation measures or ECM and thus observed that there are big differences in case of energy savings. There are also some energy modeling software which has been utilized in large aspect by the engineers as well as the designers for simulating the predicting the buildings’ energy performances while doing the design and thus they are the better device to help in making decisions with better information about any appropriate systems on behalf of the buildings (Feng, et al., 2015).
On the other hand, in real scenarios, many times the energy modelling tools have been utilized for the simplistic as well as the idealistic data inputs which would not be represented the actual systems of the buildings or the occupancy behaviour. In this process, the large discrepancies can also be observed in case of predicted as well as the actual energy performance. There is the typical average which is near about 30% of the actual performance of energy and it has been seen sometimes it has reached 100% (Yang, et al., 2016).
However, there are some natural resources like oil which has a high price and those are decreased for day to day usage causing the rise of global warming in the developed countries and the only way to escape is that to reduce the energy consumption. In this scenario, the commercial buildings of United States are found to be consumed near about 18% of the natural energy. As a result of which there is emissions of the global greenhouse is like 12% in a year. Therefore, there is the respect, need for evaluation or would understand the energy performances at the time of design as well as there is needed to provide a good effort for increasing the efficiency as well as the conservation (Zikos, et al., 2016).
At present, there are wide varieties of building energy simulation programs which are thus available for different scenarios like, Virtual Environment, ESP-r, DOE-2, BLAST, ICE, TRNSYs, or EnergyPlus etc. The range of complexity levels is thus measured from the steady-state calculations to the sophisticated programs. This also includes CFD simulation. This type of simulation is assumed to have the theoretical representation of the status as well as the operation of the building. It is thus cannot be perfectly replicated the real dynamics which will govern the energy usage. As a simple example, it can be observed that the actual climate may vary as per the meteorological data available and the systems will not vary exactly as per the expectation from the curves with the load operation. This building performance also has found to be varied with a duration measured for the working hours, the age of plant along with the scheduled maintenance activity that is scheduled earlier. Finally, the energy performance may be affected by the actual behaviour in case of building occupants (Crossman, 2015).
Moreover, the building simulation tools are also depended on the heat transfer as well as the thermodynamic equations. Also there is a direct dependency on the occupancy behavior model which includes the operation of windows, along with lights and other blinds inside the buildings and that is also based on the prior fixed scheduled or the predefined rules like the situation of open window for the certain exceeding of indoor temperature ( Daniel Miessler, 2017). Additionally, there are other tools those are often reproduced with the building dynamics while using the numerical approximations for the equations models based on the deterministic behaviours and they would be fully predictable as well as repeatable. In that context, “occupant behaviour simulation” is found to be referred for the computer simulation which in turn generates the “fixed occupant schedules”. This also represents the fictional behaviour for the building occupant on the sue course of a whole day. Thus the problem is found within the occupancy behaviour and those parameters have an impact near about 30% to 100% discrepancies in the case of actual as well as predicted energy usage for the buildings (Kwok, et al., 2011).
OBJECTIVES OF RESEARCH
Thus the objective of this research paper is discussed on the methodology for deriving the occupancy model in case of building energy simulation where there is a detailed discussion on the energy monitoring as well as the energy management system inside the building.
The development of the model thus follows the following steps:
1. The effects of the occupancy rates are thus quantified on the heat energy in case of an office or other campus buildings.
2. The energy consumptions are thus monitored in different situations of the day, like, in office hour from 4 am to 4 pm, in some weekends or any holidays and thus a methodology is being developed as well as validated for deriving the occupancy schedules.
3. It is also needed to examine the energy simulation accuracy with the help of the occupancy schedules which is derived from the heat energy as filtered and in an hourly manner in the case of unavailability of the sub-metering system.
It has been observed that in a tropical climate any person can have a feeling of uncomfortable with increased air temperature along with the radiant temperature and the nature of humidity. For that reason, it has been recommended that in 230C to 260C there is the relative humidity as 60% to 70%. The air condition of those buildings is found tight. The presence of fresh air thus has the controlled over the optimum quality with the indoor air and in that way the occupancy level can be controlled. If there is a number of people inside the buildings, more fresh air intake thus required.
Moreover, it has been observed that in the low temperature there is the low humidity and that will uncomfortable, unhealthy as well as expensive. In case of office, if the inside air temperature is remaining 220C to 230C that will affect the people and thus they may be found to have the dress up for warmer clothes. As per the effect, the cooling load can be increased increase inside the building. On the other hand, the quality of indoor air temperature can be further improved with the usage of the electronic air cleaners although there is the normal fibre for cleaning the incoming air extracted through the particles of pollutants (ERA-ENVHEALTH, 2012).
It has been also found that there is a direct impact on building energy consumption for the occupancy behaviour, or the occupancy rate and their availability. On the other hand, the electricity consumption has shown the important as well as positive correlation like 63-69% in case of the occupancy rates for different types of buildings. Thereby, it is also considered that the activity of the occupants in buildings has the impact on some required cooling load for the buildings as well as the performance of some results in simulation shows the occupancy data which will help to predict the critical role in case of building cooling load. There has the significant improvement in the predictive accuracy of the cooling load models. In the similar fashion, for lowering the building temperature, Consumption patterns have been utilized in case of deriving the occupancy schedules and to improve the accuracy of the energy simulation results (Cleverism, 2016).
It has also been found that the case studies are basically depended on the impact of the occupants’ behaviour as well as the importance of those parameters of occupancy in case of energy simulation. Mostly, those studies have been written based on the actual building's data in case of measured occupancy. In this study, thus a simplest and acceptable methodology has been derived from the occupancy schedules for the practical usage along with limited resources for the sake of the projects (Chen, et al., 2013).
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