Future Weather Data Research Articles

Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions

Net-zero energy building (NZEB) is widely considered as a promising solution to the current energy problem. The existing NZEBs are designed using the historical weather data (e.g. typical meteorological year-TMY). Nevertheless, due to climate change, the actual weather data during a NZEB’s lifecycle may differ considerably from the historical weather data. Consequently, the designed NZEBs using the historical weather data may not achieve the desired performances in their lifecycles. Therefore, this study investigates the climate change impacts on NZEB lifecycle performance (i.e., energy balance, thermal comfort and grid interaction) in different climate regions, and also evaluates different measures' effectiveness in mitigating the associated impacts of climate change. In the study, the multi-year future weather data in different Chinese climate regions are firstly generated using the morphing method. Then, using the generated future weather data, the lifecycle performances of the NZEBs, designed using the TMY data, are assessed. Next, to mitigate the climate change impacts, different measures are adopted and their effectiveness is evaluated. The study results can improve understanding of the climate change impacts on NZEB lifecycle performance in different climate regions. They can also help select proper measures to mitigate the climate change impacts in the associated climate regions.

Hourly weather data projection due to climate change for impact assessment on building and infrastructure

The global climate change research has been conducted for a few years in various professional communities. In the building industry, researchers usually investigate the future building energy demands due to the climate change by simulation software. The input files to the simulation software includes projected weather data and building models. Although there exist a few mathematical methods to project the future weather, the morphing method is the most well-known among them. In the meantime, the simulation software and weather data are in a variety of formats depending on country of origin and/or simulation package. In order to provide both the research and the professional communities the possibility to undertake climate change impact assessments on buildings, coastal engineering and construction, land use and other related areas, this study develops the web-based application Weather Morph: Climate Change Weather File Generator accessible to generate the future weather data for more than 2100 locations throughout the world for all four IPCC (Intergovernmental Panel of Climate Change) emission scenarios in the three future time slices of the 2020s, 2050s and 2080s. The output of the application is projected future weather datasets in formats TMY2 and EPW for general use.

Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings

In this paper, the possibilities of developing machine learning based data-driven models for the short-term prediction of indoor temperature within prediction horizons ranging from 1 hour up to 12 hours are systematically investigated. The study was based on a TRNSYS emulation of a residential building heated by a heat pump, combined with measured weather data for a typical winter season in Ljubljana, Slovenia. Autoregressive models with exogenous inputs (ARX), neural network models (NN), and extreme learning machine models (ELM) are considered. The results confirm the finding that nonlinear models, particularly the NN model trained by regularization, consistently outperform linear models in both fitting and generalization performance, so they are the recommended choice as predictive models. The availability of future weather data considerably improved the predictive performance of all the tested models. Besides data about the future outdoor temperature, also data about future expected solar radiation significantly improve predictions of temperature in buildings. The linear models required embedding dimensions of 24 hours for accurate predictions, whereas the nonlinear models were not very sensitive to the use of past data. Nonlinear models required about three months of training data to reach good predictive performance, whereas the linear models converged to accurate predictions within six weeks. The RMSE prediction errors, averaged over all the data sets and all the prediction horizons, are within the range between 0.155 °C for the linear ARX model (in the case of no future available weather data), and 0.065 °C for the neural network model (in the case of available future weather data).

Climate change: Projections and implications to building energy use

Changes in climate have significant impacts on built environment. Many of the potential effects of climate change on the building sector are not well known. Previous studies have used a small number of climate projection models and scenarios, with the majority only using one or two models with multiple scenarios. This study identified and analyzed twenty-three climate models with one or more scenario for each model for total of fifty-six model scenarios. Future hourly weather data between 2011 and 2099 was generated with the morphing algorithm for seven climate zones in the US. Using cooling degree day (CDD) and heating degree day (HDD) as energy impact indicators, the study revealed that different climate models (even within the same RCP scenario) yield largely different results for building energy implications. To simplify application, four reference climate models were selected to represent the full range of the fifty-six model outputs, whose accuracy was validated using historical data. The study explored the impacts of climate changes on energy use of five typical US building types in Ann Arbor, MI, as a demonstration, which presented a general trend of site energy decrease and source energy increase for this location. The research further examined the influences of humidity and found that dry bulb temperature dominates the changes in building energy consumption and relative humidity only has a relatively larger impact on extreme cases in cooling dominated climates.

Prediction of climate variables by comparing the knearest neighbor method and MIROC5 outputs in an arid environment

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 77:99-114 (2019) - DOI: https://doi.org/10.3354/cr01545 Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment Hamid Reza Golkar Hamzee Yazd1, Nasrin Salehnia2,*, Sohrab Kolsoumi2, Gerrit Hoogenboom3 1Ferdows Branch, Islamic Azad University, PO Box 9771-848664, Ferdows, Iran 2Ferdowsi University of Mashhad, PO Box 9177-949207, Mashhad, Iran 3Institute for Sustainable Food Systems, University of Florida, PO Box 110570, Gainesville, Florida, USA *Corresponding author: salehnia61@gmail.com ABSTRACT: The goal of this study was to compare the ability of the k-nearest neighbors (k-NN) approach and the downscaled output from the MIROC5 model for generating daily precipitation (mm) and daily maximum and minimum temperature (Tmax and Tmin; °C) for an arid environment. For this study, data from the easternmost province of Iran, South Khorasan, were used for the period 1986 to 2015. We also used an ensemble method to decrease the uncertainty of the k-NN approach. Although, based on an initial evaluation, MIROC5 had better results, we also used the output results of k-NN alongside the MIROC5 data to generate future weather data for the period 2018 to 2047. Nash-Sutcliffe efficiency (NSE) between MIROC5 estimates and observed monthly Tmax ranged from 0.86 to 0.92, and from 0.89 to 0.93 for Tmin over the evaluation period (2006-2015). k-NN performed less well, with NSE between k-NN estimates and observed Tmax ranging from 0.54 to 0.64, and from 0.75 to 0.78 for Tmin. The MIROC5 simulated precipitation was close to observed historical values (-0.06 < NSE < 0.07), but the k-NN simulated precipitation was less accurate (-0.36 < NSE < -0.14). For the studied arid regions, the k-NN precipitation results compared poorly to the MIROC5 downscaling results. MIROC5 predicts increases in monthly Tmin and Tmax in summer and autumn and decreases in winter and spring, and decreases in winter monthly precipitation under RCP4.5 over the 2018-2047 period of this study. This study showed that the k-NN method should be expected to have inaccurate results for generating future data in comparison to the outputs of the MIROC5 model for arid environments. KEY WORDS: RCP4.5 · Statistical downscaling · Delta method · Ensemble · LARS-WG · Lut Desert Full text in pdf format Supplementary material PreviousNextCite this article as: Golkar Hamzee Yazd HR, Salehnia N, Kolsoumi S, Hoogenboom G (2019) Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment. Clim Res 77:99-114. https://doi.org/10.3354/cr01545 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 77, No. 2. Online publication date: February 21, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

Impacts of future weather data typology on building energy performance – Investigating long-term patterns of climate change and extreme weather conditions

Patterns of future climate and expected extreme conditions are pushing design limits as recognition of climate change and its implication for the built environment increases. There are a number of ways of estimating future climate projections and creating weather files. Obtaining adequate representation of long-term patterns of climate change and extreme conditions is, however, challenging. This work aims at answering two research questions: does a method of generating future weather files for building performance simulation bring advantages that cannot be provided by other methods? And what type of future weather files enable building engineers and designers to more credibly test robustness of their designs against climate change? To answer these two questions, the work provides an overview of the major approaches to create future weather data sets based on the statistical and dynamical downscaling of climate models. A number of weather data sets for Geneva were synthesized and applied to the energy simulation of 16 ASHRAE standard reference buildings, single buildings and their combination to create a virtual neighborhood. Representative weather files are synthesized to account for extreme conditions together with typical climate conditions and investigate their importance in the energy performance of buildings. According to the results, all the methods provide enough information to study the long-term impacts of climate change on average. However, the results also revealed that assessing the energy robustness of buildings only under typical future conditions is not sufficient. Depending on the type of building, the relative change of peak load for cooling demand under near future extreme conditions can still be up to 28.5% higher compared to typical conditions. It is concluded that only those weather files generated based on dynamical downscaling and that take into consideration both typical and extreme conditions are the most reliable for providing representative boundary conditions to test the energy robustness of buildings under future climate uncertainties. The results for the neighborhood explaining the critical situation that an energy network may face due to increased peak load under extreme climatic conditions. Such critical situations remain unforeseeable by relying solely on typical and observed extreme conditions, putting the climate resilience of buildings and energy systems at risk.

Identifying suitable general circulation model for future building cooling energy analysis

These future building energy studies mainly stem from hourly based dynamic building simulation results with the future weather data. The reliability of the future building energy forecast heavily relies on the accuracy of these future weather data. The global circulation models (GCMs) provided by IPCC are the major sources for constructing future weather data. However, there are uncertainties existed among them even with the same climate change scenarios. There is a need to develop a method on how to select the suitable GCM for local application. This research firstly adopted principal component analysis (PCA) method in choosing the suitable GCM for application in Taiwan, and secondly the Taiwanese hourly future meteorological data sets were constructed based on the selected GCM by morphing method. Thirdly, the future cooling energy consumption of an actual office building in the near (2011-2040), the mid (2041-2070), and the far future (2071-2100), were analysed. The results show that NorESM1-M GCM has the lowest root mean square error (RMSE) as opposed to the other GCMs, and was identified as the suitable GCM for further future climate generation processing. The building simulation against the future weather datasets revealed that the average cooling energy use intensity (EUIc) in Taipei will be increased by 12%, 17%, and 34% in the 2020s, 2050s, and 2080s, respectively, as compared to the current climate.

Climate change impact on energy balance of net-zero energy buildings in typical climate regions of China

Net-zero energy building (NZEB) is widely considered as a promising solution to the current energy and environmental problems. The existing NZEBs are designed using the historical weather data (e.g. typical meteorological year-TMY). Nevertheless, due to climate change, the actual weather data during a NZEB’s lifecycle may differ considerably from the historical weather data. Consequently, the designed NZEBs using the historical weather data may not achieve the desired performance in their lifecycles. Therefore, this study investigates the climate change impacts on NZEB’s energy balance in different climate regions, and also evaluates different measures’ effectiveness in mitigating the associated impacts of climate change. In the study, the multi-year future weather data in different climate regions are firstly generated using the morphing method. Then, using the generated future weather data, the energy balance of the NZEBs, designed using the TMY data, are assessed. Next, to mitigate the climate change impacts, different measures are adopted and their effectiveness is evaluated. The study results can improve the understanding of climate change impacts on NZEB’s energy balance in different climate regions. They can also help select proper measures to mitigate the climate change impacts in the associated climate regions.

Effect of climate change on the energy performance and thermal comfort of high-rise residential buildings in cold climates

Buildings now produce more than a third of global greenhouse gases, making them more than any other sector contributing to climate change. This paper investigates the effect of climate change on the energy performance and thermal comfort of a high-rise residential building with different energy characteristic levels, i.e. bylaw to meet current National Energy Code of Canada for Buildings (NECB), and passive house (PH) under two climate zones in British Columbia, Canada. SRES A2, RCP-4.5 and RCP-8.5 emission scenarios are used to generate future horizon weather data for 2020, 2050, and 2080. The simulation results show that for both bylaw and PH cases, the heating energy consumption would be reduced while cooling energy consumption would be increased. As a result, for the bylaw case, the total energy consumption would be decreased for two climate zones, while for PH case, the total energy consumption would be increased for zone 4 and decreased for zone 7. In addition, the number of hours with overheating risks would be increased under future climates, e.g. doubled in 2080, compared to the historical weather data. Therefore, efforts should be made in building design to take into account the impact of climate change to ensure buildings built today would perform as intended under changing climate.

The feasibility and importance of considering climate change impacts in building retrofit analysis

Current building energy use is projected to increase by 1.7% annually until 2025, and the great potential for energy reduction in existing buildings has created opportunities in building energy retrofit projects. In this research, a framework and method are proposed to evaluate the impacts of different retrofit options to existing building under climate change. A Python retrofit tool is developed to perform parametric study by running EnergyPlus under different retrofit scenarios for existing buildings. With the help of Latin-hypercube sampling (LHS) method and a joint mutual information maximization (JMIM)-based feature selection method, the energy conservation measure (ECM) that may have the most potential in reducing the energy use or the lifecycle net present value (NPV) of a target existing building can be selected. A validated data-driven model is used to predict the building’s future hourly energy use based on EnergyPlus simulation results generated by future extreme year weather data. It is demonstrated that global climate change will alter the optimal solution of future ECM combination and its influence varies from building to building, location to location. The optimal retrofit strategy of selecting the best ECM combinations under current climate condition will be subject to change in the future climate condition.

New methodology of evaluation of best management practices performances for an agricultural watershed according to the climate change scenarios: A hybrid use of deterministic and decision support models

Climate change is a primary driver that alters water pollution hotspots in watersheds, which complicates the use of Best management practices (BMPs) for diffuse pollution mitigation strategies. The objective of this study is to evaluate future changes in BMPs on total phosphorus (TP) loads to the river system, depending on climate change. Current weather data were collected from 2000 to 2010 and future weather data from 2040 to 2050 were obtained from regional climate model simulations based on Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 scenarios. This study describes various BMP implementation plans at affordable cost that are adapted to different weather conditions, current (for 2000–2010) and future climate scenarios (for 2040–2050). The Soil and Water Assessment Tool (SWAT), i.e., one of the deterministic models for watershed management, was used to estimate total phosphorus (TP) removal efficiency of various types of BMP at two crop fields (rice paddy and soybean fields) in the Yeongsan River (YR) watershed of Korea. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the artificial intelligence (AI) models for decision support framework, was applied to obtain allow for trade-off analysis between two conflict objectives, the maximum TP load reduction against implementation costs. The SWAT simulation results showed that the model performance based on the Nash-Sutcliffe efficiency and percent bias was acceptable for simulating three parameters; daily flow discharge, monthly sediment loads, and TP loads in the river. As a result, monthly sediment loads and TP loads increase remarkably under all future climate change scenarios except for July, when a comparable amount of precipitation is recorded during the present and future conditions. New implementation of BMPs, therefore, will be required for future climate change scenarios to achieve 50% TP load reduction in the given watershed condition. Interestingly, parallel terraces which showed good efficiency in reducing TP loads at both fields under the current weather condition cannot be effective any more under the worst-case weather scenario in the future climate. In such a case, detention ponds can be proposed as BMP alternative to parallel terraces. Overall, this study not only demonstrates that watershed management plans using BMPs should be adjusted according to climate change scenarios, but also that the hybrid use of SWAT model and AI can help in refining existing and future BMPs at affordable cost.

Estimation of the Source Apportionment of Phosphorus and Its Responses to Future Climate Changes Using Multi-Model Applications

The eutrophication issue in the Yangtze Basin was considered, and the phosphorus loads from its tributary, the Modaoxi River, were estimated. The phosphorus flux and source apportionment of the Modaoxi River watershed were modeled and quantified, and their changes with respect to future projected climate scenarios were simulated with multiple model applications. The Regional Nutrient Management (ReNuMa) model based on Generalized Watershed Loading Functions (GWLF) was employed as a tool to model the hydrochemical processes of the watershed and thereby estimate the monthly streamflow and the phosphorus flux as well as its source apportionment. The Long Ashton Research Station Weather Generator (LARS-WG) was used to predict future daily weather data through the statistical downscaling of the general circulation model (GCM) outputs based on projected climate scenarios. The synthetic time series of daily precipitation and temperatures generated by LARS-WG were further used as input data for ReNuMa to estimate the responses of the watershed hydrochemical processes to future changed climate conditions. The results showed that both models could be successfully applied and that the future wetter and warmer climate trends would have generally positive impacts on the watershed phosphorus yields, with greater contributions coming from runoff. These results could provide valuable support for local water environmental management.

Cooling and heating energy performance of a building with a variety of roof designs; the effects of future weather data in a cold climate

Building engineers commonly use the Typical Meteorological Year (TMY) weather data for simulation and design purposes. However, the nature of TMY in excluding weather extremes makes them less suitable to investigate the effect of potential climate change on building design as climate change likely increases the frequency and magnitude of those extreme conditions. The current practice of designing buildings has lacked a clear method to incorporate future climate change trends. An approach is used to compare present weather simulation results of a commercial building with varying roof reflectance and insulation thermal resistance parameters with future year-by-year results which are affected by potential climate change. Future weather data for year-by-year simulations is obtained by “morphing” historical weather data with a General Circulation Model (HadCM3). Mean energy consumption and optimal roof configurations are discussed with regards to climate change over the study period, and are compared to results obtained with TMY data. Results show that increased roof solar reflectance always lead to less mean and less variant cooling energy consumption. The study shows the importance of considering possible future climate scenarios and in building energy performance design.

Development of future weather data using global warming projection: Research on future weather data for designing building and equipment which are adaptable to climate change

AbstractIt would be useful for building designers to consider future climate change by using building thermal simulations to construct a building adaptable to global warming. Future weather data including future changes to climate are needed as input data to conduct building thermal simulations. In this report, a method to produce hourly future weather data is presented. The method combines real current hourly weather data with results from climate models. Hourly future weather data at 833 stations for 2040s and 2090s have been developed and applied to heat load calculations of office buildings.

An investigation into the energy performance gap between the predicted and measured output of photovoltaic systems using dynamic simulation modelling software—a case study

The use of solar energy can help reduce CO₂ emissions and dependency on fossil fuels, and using Solar Photovoltaic (PV) systems to generate electricity is a popular route to decarbonisation in the UK. To help achieve the targets set out in the Climate Change Act, building service consultants often use EDSL Tas, a dynamic modelling software, to simulate PV systems and integrate the energy output results into the overall energy performance of a building. There is, however, a clear performance gap between the measured and predicted energy output. There are many causes for the potential deviation of results, although the most influential in relation to energy performance is the use of weather data, future climate change, adverse weather conditions and environmental factors affecting the PV array. The results through a case study indicated an 8.6% higher measured energy output from the installed PV system although the performance gap has little detrimental effect regarding achieving Building Regulation compliance, but could lead to the unreasonable design of the PV system and inappropriate use of capital investment. Further simulation using projected future weather data from several different climate change scenarios was undertaken. 2020, 2050 and 2080 with low, medium and high emission scenarios indicated that the PV array would increase energy output by up to 5% by 2080 compared with using current weather data, indicating a rise in PV energy output in relation to increased CO₂ emissions. This is due to a projected reduction in cloud cover and increased downward shortwave radiation.

Cooling Energy Implications of Occupant Factor in Buildings under Climate Change

Occupant factor is a critical element in the overall energy consumption of buildings. This study aims to reveal how occupant factors influence the energy consumption of example buildings in Seoul, Tokyo, and Hong Kong under climate change projections, and to prioritize factors with energy saving potential for buildings in consideration of future climate change. The study finds that the cooling degree-hours base of 23.3 °C in the three cities sharply increases with future climate change by analyzing future hourly weather data produced herein. Simulations are made with EnergyPlus Runtime Language (Erl) for modeling occupant behavior. The simulation results reveal that a dynamic thermostat control based on an adaptive comfort model is an effective method to reduce cooling energy consumption under future climate change, reducing cooling energy consumption by up to 18% in some instances. In particular, we reveal that a combined application of the adaptive comfort control, nighttime ventilation, and the use of occupancy-based lighting and equipment result in reducing cooling energy consumption by 28%. The outcomes of this study are potentially useful in providing cost-effective solutions to adapt buildings for future climate change with simple modifications to occupant behavior. Also, the roles of renewable energy are briefly discussed.

Critical Analysis of Software Tools Aimed at Generating Future Weather Files with a view to their use in Building Performance Simulation

Two software tools, namely CCWorldWeatherGen and WeatherShift™, are today available on the market and enable individual end-users, to generate future projection weather data that can be used for executing building performance simulation. These software tools have been developed based on different assumptions. Therefore, the outputs of the two tools were generated and compared both graphically and using statistical methods to get to a better understanding of their differences and, hence, to identify possible consequences when applied to building performance simulation. The results suggest that, depending on the purpose of the design, care should be taken in using the above-mentioned tools.

Assessing climate change adaptations for community-scale water resources using a low-frequency weather generator

Water adaptation strategies are necessary for decreasing climate change impacts on local societies. This study models a community water supply system as a distributed adaptation measure for decreasing the loading of the centralized water supply system. The modeled community water supply system addresses both supply and demand at the community scale using constructed wetlands, rainwater harvesting systems, household storage, and paddy fields. The purpose of this study was to evaluate a community water supply system in various climate change scenarios using three performance indicators: water saving efficiency (WSE), tolerance duration (TD), and water use efficiency (WUE). A low-frequency weather generator (LF-WGEN) was proposed and used to produce future weather data for evaluating the impacts of climate change. LF-WGEN generates future weather data based on climate scenarios derived from the outputs of general circulation models (GCMs, such as HadGEM2-AO and CSIRO-Mk3-6-0) which project current and future climate based on different representative concentration pathways (RCPs). The results indicate that low impact development (LID) modules have better performance in the first growth period and may maintain domestic functionality under climate change. All indicators show positive correlations with rainfall in most of the climate scenarios. However, with slight decreases in predicted rainfall, the WUE and WSEagri showed negative correlations to rainfall in the RCP 2.6 HadGEM2-AO and RCP 4.5 CSIRO-Mk3-6-0 climate scenarios. This could be due the lower number of consecutive dry days predicted by the LF-WGEN causing a slightly increased wetland inflow that would then decrease the irrigation water supply. Overall, the community water supply system showed strong performance in the various climate scenarios. Further study of the community water supply system needs to include an optimization of adaptation measures for practical applications.

Impact of Climate Change on the Preferred Season for Outdoor Water Activities

The purpose of this study is to predict changes in the season for outdoor water activities due to climate change. Nine public outdoor swimming pools in three megacities of South Korea (Seoul, Daegu, and Busan) were selected as study sites. To determine the preferred weather conditions for outdoor water activities, the preference functions of weather elements for outdoor water activities were structured by finding the best-fitting lines with the Z-score of the number of visitors, which is calculated for each swimming pool each year, and the inflection points or the stabilized point of preference functions are set as thresholds for preferred weather conditions for outdoor water activities. To predict changes in the preferred season for outdoor water activities, future weather data for the 2030s, 2060s, and 2090s derived from RCP (Representative Concentration Pathway) scenarios are adapted to the thresholds of preferred weather conditions. The results of this study show that the preferred temperature range for outdoor water activities is: 24.6 °C to 35.0 °C in Seoul, 25.5 °C to 35.5 °C in Daegu, and 27.4 °C to 34.4 °C in Busan, and that the maximum threshold for precipitation is: 36.5 mm in Seoul, 31.5 mm in Daegu, and 26.5 mm in Busan. The results of this study show that the preferred season for outdoor water activities will expand compared to its current duration due to warmer temperatures in the future, and the preferred period for these activities will shift from June to September to May to June and September to October due to extremely hot weather in July and August beginning in the 2030s. The results of this study imply that there will be major changes in the demand and operation of outdoor water activities due to climate change, making it necessary to begin preparations to combat and respond to climate change.

Mixed-mode ventilation and air conditioning as alternative for energy savings: a case study in Beirut current and future climate

The aim of this work is to assess the use of mixed-mode ventilation for a typical office building in Lebanon and consequently reduce Heating Ventilation and Air Conditioning (HVAC) energy consumption in the observed current and under the future projected climatic conditions. Mixed-mode cooling is considered a compromise between the insufficient natural ventilation and the expensive year round-operated HVAC. A control algorithm is set for windows and HVAC system to ensure mixed-mode operation. Dynamic simulations are performed on a typical office building in Beirut City under the mixed-mode operation in the present and the future using commercial IES-VE software. The results of the software were validated against measured HVAC and total energy consumption of the typical office base case with conventional mechanical system. The results of the simulations are evaluated in terms of potential reduction in energy consumption under the present and the future weather data. Finally, a lifecycle cost analysis is performed for the proposed system, and its payback period is computed. Under present construction practices and weather data, 31% annual energy savings were achieved using mixed-mode system. Under future 2050s projected weather data, annual energy savings of 21% was attained with a payback period of 3.8 years.