To perform analysis on how to make dwellings resilient to heat waves, we have gone through a lot of different research journal papers. We came across to different types of methods and analysis that were carried out in different countries and cities with various parameters. One of the studies uses the following methods,
(Navid Delgarm , Behrang Sajadi, Saeed Delgarm) In the first paper(Multi-Objective optimization of building energy performance and indoor thermal comfort: A new method of artificial bee colony) the single – objective and multi objective optimization analyses of the total annual building electricity consumption and the predicted percentage of dissatisfied (PPD) are investigated to bring down the total energy costs as well as the thermal discomfort in the four major climate regions of Iran which are temperate , warm- dry , warm – humid and cold.
Building sectors are the largest energy consumers across other sectors in the world, accounting to that 40% of energy use and 36% of carbon dioxide emissions all over the world. However, the large proportion of the energy used in the buildings around the world is for the thermal comfort. Therma comfort is the condition of the mind that describes as the satisfaction of the mind with the thermal environment. And is very important for the health and wellbeing as well as for the productivity. Generally, there are two main models to evaluate the thermal comfort including the static model (PMV-PPD) and the adaptive model.
Where static model was developed by Fanger by means of heat balance equation and empirical studies on human bodies. In addition, Fanger also presented another thermal comfort index called Predicted Percentage Dissatisfied (PPD) to predict the percentage of the occupants who are dissatisfied with the environmental conditions where they feel too cold or too warm.
The results of the proposed simulation- based optimization method is presented based on two approaches. In the first approach single- objective optimization is conducted only at Tehran located in the Temperate climate zone. In this approach the total energy consumption and PPD are individually minimized by means of single – objective ABC optimization technique. The main objective here is to see how diverse criteria may influence the optimal solution.
In the second approach, a double objective optimization is conducted to understand the mutual impacts of the variables on total energy consumption and PPD. In the later approach , MOABC optimization algorithm is employed to achieve the Pareto optimal fronts for different weather conditions of Iran. In addition, TOPSIS decision making approach is implemented to select the final optimum solution.
This study introduced an efficient simulation – based multi- objective optimization procedure to promote the building energy performance and the indoor thermal comfort. In this paper, a multi objective particle swarm optimization (MOPSO) MATLAB code was integrated with EnergyPlus and jEPlus tool as a programming interface. This method gave fast and effective tool to detect the optimal solutions of building design optimization problems and help building designers to decide on the most proper alternatives. The presented optimization method was tested on a single room office. The total building energy consumption and predicted percentage dissatisfied were selected as two objective functions, which are strongly nonlinear, coupled and conflicting. The single and multi-objective optimization analysis were carried out in the result. The optimal solution obtained from he double-objective optimization problem were given as Pareto fronts to realize the interactions between the objectives in different weather conditions. Ultimately, TOPSIS decision- making method was applied to obtain the final optimum design. The single – objective and double objective optimization analysis obviously showed that the total energy consumption is completely opposite to PPD. The lower energy consumption led to increasing the PPD value. Additionally, the results of double- objective optimization problem obtained by TOPSIS decision main g method showed that for different weather conditions, the total energy consumption increases about 2.9-11.3 %, while the PPD brings down greatly about 49.1-56.8 % respect to the base design. Based on the figures by the Pareto fronts as well as the final optimum design, the climate had a great impact on the building energy consumption and PPD would be significantly pointed by choosing the best building design configuration according to each the climate.
(D.P. Jenkins , V. Ingram, S.A. Simpson, S. Patidar) In the second paper (Methods for assessing domestic overheating for future building regulation compliance), it shows the different types of assessing the overheat in domestic sectors of UK with a case study house that has been already assessed by the Low Carbon Future Project. In this study there will be comparison of the assessing methods or processes like the SAP , RdSAP , IES-VE(Integrated environmental solutions virtual environment) and Probabilistic climate projections. These 4 options / methods have been used with different climatic conditions to observe if there is a consistency in the output and then change its variables to make a standardised overheating valuation. The case study house has been used in a program called the ESP-r which is similar to one of the methods we using that is IES regardless of the different user interface and the calculation methods.
Moving forward , the steady state tool to assess overheating are basically different in terms of their calculation engines and viewpoint at the end of assessment.
However , rule of thumb has been used to determine the overheat assessment in SAP and RdSAP and there will be alternative climate conditions used to assess rather than using the recommended data from the SAP and RdSAP tools. The climates used are the from 2005 to 2030 for Edinburgh and London , where these climates data are used from the TRY and DSY file. However , at the time of using these tools for the real time , the user doesn’t use the data from the TRY and DSY files and rather use the SAP recommended data within the tool. Because this study is all about assessing overheating methodology , the authors are using the climate values from the TRY and DSY files .
In the SAP tool assessment ,the calculation includes contributing factors to overheating; namely, heat loss through ventilation and building fabric, solar gain, heat storage through thermal capacity and mean summer external temperature for the months of June–August.
Whereas , the RdSAP uses the same procedure as the SAP but the input values may be different than rather than being same as in SAP. RdSAP is used for the assessment of overheating in the existing buildings , where the variables required to assess the energy is not available and hence uses the default variables to use in the SAP methodology. These assumptions are made in regards to the orientation and the area of window and can result in various level of solar gain in the SAP and RdSAP assessment. Thermal mass in RdSAP is
also default which results in the increment of thermal mass differently in both the versions hence the results may be inconsistent for these two tools. Bothe SAP and RdSAP had the default climate information. Manual exchange of these climate information is possible but cannot be practiced as a standard building code. After the assessment of calculating the threshold temperature using the tools SAP and RdSAP , the results are clearly estimating that SAP has showed the threshold temperatures of 1.3 to 1.5 degrees higher than that of RdSAP , which means there will be more overheating dwellings according to SAP predictions.
It is also noticeable that, although the default climate data in RdSAP/SAP suggests a slightly cooler London baseline than the TRY, the default Edinburgh climate in SAP produces a similar level of overheating to the DSY. This lack of consistency is something that future versions of SAP and RdSAP might choose to investigate, examining the problem for a range of housing types. However , when using the IES-VE simulation tool which is highly used and recommended in building industry it shows that the threshold temperature in London will exceed eventually in future when compared to the threshold temperature in Edinburgh will not exceed the 28 degree temperature by any percentage regardless of what 1% increment in London scenario. Now , using the probabilistic climate projection .For a given building, this method still requires the use of a single dynamic simulation (such as ESP-r or IES-VE) that calibrates the regression equation for that speci?c building. Once calibrated, the regression equation can quickly and ef?ciently estimate the hourly temperature pro?le of the building for, theoretically, any climate description. Each climate scenario will be represented by 100 equally probable climate ?les, all of which are used by the regression tool. Each of these 100 climate ?les will have an individual overheating result (e.g., number of hours above a threshold), and these 100 results can be put into a frequency curve to represent the probability of that building overheating for that speci?c climate scenario. The tool can be used to process more than 100 climate ?les per scenario, but previous work has suggested that 100 ?les are a suitable number for representing the variation within each climate scenario.
Different overheating methodologies can produce signi?cantly different outputs, both in terms of quantity and format, and this is likely to affect the choices made by designers of both new dwellings and refurbishments of existing dwellings. Such choices are being made for dwellings now, but will impact the performance of those dwellings in decades to come, hence the use of future climate projections for this study. While no single method is being proposed as optimum from this study, it is clear that a suitable compromise has to be made between a format that is useful and understandable to potential users while also using reliable building physics and climate information.
(D.M. King, B.j.C. Parera)In the 3rd paper(Morris method of sensitivity analysis applied to assess the importance of input variables on urban water supply yield) , Yield plays a central role in the processes, practices, management and operation of urban water supply systems. Improved accuracy of the yield estimate is important and can be obtained by improving knowledge of important variables. Yield is typically estimated via computational simulation using the entire sequence of available historic climate data. Sensitivity analysis provides a framework and many techniques that can identify important variables in a computational model. Using the Morris method, this paper investigates the importance of input variables used in the estimation of yield of urban water supply systems. The Barwon urban water supply system in Australia is used as the case study.
The Sensitivity Analysis (SA) framework adopted in this paper is to consider multiple climate scenarios of various simulation lengths over which the importance of the system policy variables are determined using SA. This SA framework allows the effect of climate variability and simulation length on the importance of input variables to be explicitly assessed whilst retaining cross-correlations inherent in climate variables and avoiding perturbing the times-series variables. In effect, the framework can then be used to determine if climate variability and/or simulation length affects the importance of the controllable system policy variables used in the estimation of yield. The Morris method of SA (Morris, 1991) was selected for use in this paper as it is a reliable and ef?cient SA technique shown to be successful when applied to a water supply system planning model (King and Perera, 2010). This paper begins by presenting a discussion of the SA framework adopted in this study for water supply systems, followed by a discussion of the Morris method, including application ,indices and extensions. A description of the Barwon urban water supply (used as the case study) is then presented including the climate scenario selection, and a discussion of the system policy variables and their methods of perturbation. The SA results are then described. Finally the main ?ndings and conclusions of the paper are summarised with resulting recommendations.
Barwon Water Corporation is currently the largest regional water authority in Victoria (Australia) supplying water and sewerage services to 275,000 permanent residents over
8100 square kilometres. The region of operation, covers a regional (rural) and coastal area in south-west Victoria, Australia. The operational headworks of the Barwon Water Corporation, hereinafter simply termed the Barwon system, consists of over 5000km of pipes, six major reservoirs, six water treatment plants and nine water reclamation plants. Water is sourced from two rivers and their catchments, and a number of groundwater sources.
Three sets of experiments were performed, an individual variable experiment and two grouped variable experiments. Grouping of related variables was done to gain insight into system behaviour, such as any synergism or cancelling out that may occur. All experiments were performed using a 50 trajectory, eight level (p = 8), D = 4 Morris design on each of the 20 climate scenarios. Fifty trajectories were deemed to be suf?cient to ensure that convergence of SA indices were satis?ed, whilst p and D were selected to provide a range of possible sampling points and a wide variable perturbation. The work done in King and Perera (2010) showed that little was gained by averaging results from experiments using different number of levels and different D values. As found with the individual variable experiment, the most important input variables in all scenarios over all simulation
lengths were the supply reliability and the minimum storage thresholds. The upper RRC position, upper RRC curvature variables, target curves and base demand also showed importance across most scenarios.
Presented in this paper is an application of the Morris method of sensitivity analysis to determine the individual and groups of input variables that are most important in estimating the yield of urban water supply systems. This study considered the Barwon urban water supply system in Australia. The sensitivity analysis was based on a climate scenario framework where 20 climate sequences were selected from within the available historic data over four simulation lengths. Multiple scenarios of each simulation length were either selected based on the total stream?ow volume entering the six reservoirs of the Barwon system, or generated by a shuf?ing (or recycled) approach. The sensitivity analysis using the Morris method was performed on all scenarios considering individual variables and two different groupings of variables.
The results given in this paper demonstrate that using a single climate scenario in the estimation of yield can give unrepresentative outcomes for the yield estimate and also of the possible behaviour of the system under a different climate scenario. Further
investigation into the sensitivity of estimation of yield of an urban water supply system to the inputs and climate variability will be conducted using a high detailed and quantitative sensitivity analysis technique, such as the variance based Fourier Amplitude Sensitivity Test.