Professor Moncef Krarti's Research Group

The group led by Professor��Krarti has a wide range of research interests related to improving the energy efficiency, sustainability, and resiliency of both new and existing buildings. Some of the projects supervised by Prof. Krarti include:

Dynamic and Interactive Building Envelope Systems (Dynamic Insulation for Glazing, Dynamic Insulation Materials, Dynamic Cool Roofs)
Existing Building Retrofits (Auto-Calibration, M&V Analysis Tools)
Building Integrated HVAC Systems (Thermo-Active Foundations, Evaporative Wall Cavities, Ventilated Slabs)
Daylighting/Lighting Controls (Simplified Analysis Tools and Optimal Controls for Daylighting Systems, Optimal Lighting Circuiting and Desk Layout)
Resilient Sustainable Design (optimization for high resiliency and energy efficiency for single buildings and communities)
Use of Satellite Data for Building Energy Use Estimation (Spatial Distribution of Electricity and Fuel Consumption in Urban Centers)
Renewable Energy Technologies (Optimization Tool for RE Technologies )
Energy Productivity for Buildings (Analysis of Multiple Benefits of Large-Scale Energy Efficiency Programs)
Smart Building Energy Systems (Applications of Machine Learning to Building Operation Diagnostics and Controls)
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For office buildings, the integrated AES can achieve annual savings in space cooling up to 51% and 109% in total energy demand allowing office buildings to reach net-positive energy status especially in temperate climate zones.

Adaptive Building Envelope Technologies
In this project, the energy performance of integrated adaptive envelope systems (AES) is evaluated when applied to US residential and commercial buildings. Three main technologies are part of the AES including cool roofs, movable PV-integrated shading devices (MPVISD), and switchable insulation systems (SIS). For the study, the AES is operated to minimize annual heating and cooling energy uses. The analysis results clearly indicate that the integrated AES have high potential for cooling energy savings for both residential and commercial buildings. For residential buildings, MPVISDs offer the highest contribution followed by attic and walls integrated SISs. Overall, the integrated AES allows on-site electricity generation and offers savings between 234 kWh/year and 949 kWh/year in cooling energy depending on the US climate. The deployment of AES alone allows US homes to almost reach net-zero energy designs especially in mild and hot climates.

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For office buildings, the integrated AES can achieve annual savings in space cooling up to 51 percent��and 109 percent in total energy demand allowing office buildings to reach net-positive energy status especially in temperate climate zones.

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Energy Performance of Thermoactive Foundations
Thermo-active foundation (TAF) systems represent energy efficient alternatives to heat and cool buildings. The energy performance of TAFs is evaluated as part of this project when applied to both residential and commercial building. First, a transient three-dimensional finite difference numerical model is developed for the analysis of thermo-active foundations. The numerical model predictions are then validated against experimental data obtained from laboratory testing. Using the validated numerical model, G-functions for TAFs are generated and integrated into whole-building simulation analysis program, EnergyPlus. A comparative analysis is carried out to evaluate TAF systems compared to conventional ground-source heat pumps (GSHPs) to provide heating and cooling for multi-family residential buildings. For residential buildings, the analysis compares the cost-effectiveness of TAFs and GSHPs to meet heating and cooling needs. Due to lower initial costs associated to the reduced excavation costs, it is found that TAFs offer a more cost-effective than GSHP systems to heat and cooling multi-family residential buildings.

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Automated Energy Auditing Tool
To make the energy auditing process more effective and less time consuming, a tool has been developed to automate the calibration of energy models and determine optimal energy efficiency measures to retrofit buildings. The tool is based on a Bayesian and Gaussian Processing to develop efficient emulators to calibrate energy models for existing buildings. The emulator-based approach is applied to identify unknown parameters of building energy models used as part of the automated energy auditing tool. The Bayesian approach is considered to expedite the parameter identification suitable for the development of emulators capable of accurately predicting building energy consumption. In addition, a Gaussian Processing based random selection approach is used to generate sets of training data needed for the development of parent and child emulators. The tool has been applied to calibrate detailed energy models for several existing buildings using monthly utility data.

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Large-Scale Retrofit Analysis
The study involves the development of existing building stock models using an engineering bottom-up approach. The models are suitable for evaluating the impact of any energy efficiency or demand-side management program targeted for the existing buildings. The approach has been used to account for the make-up and the characteristics of the existing stocks using building prototypes including their type, vintage, and location. The model relies on a detailed building simulation tool to predict hourly energy consumption. The building stock model predictions have been verified against actual reported data for energy consumption levels. As one applications of the building stock modeling approach, the benefits of a series of energy retrofit measures and programs are evaluated for existing Saudi housing stocks. The analysis results clearly indicate that large-scale implementation of retrofit programs for existing housing stock is cost-effective and has a wide range of economic, environmental, and social benefits. A full and targeted implementation of optimal retrofit programs can reduce the annual electricity consumption of the Saudi residential sector by 50% in addition to similar decreases in carbon emissions and electricity generation capacities. The analysis results clearly indicate that for the retrofit program to be effective, the energy efficiency measures need to be tailored to the housing type, vintage, and location.

Use of Night Satellite Imagery for Estimating Building Energy Consumption
Despite the importance of geospatial analysis of energy use in buildings, the data available for such exercises is limited. A potential solution is to use geospatial information, such as that obtained from satellites, to disaggregate building energy use data to a more useful scale. Many researchers have used satellite imagery to estimate the extent of human activities, including building energy use and population distribution. Much of the reported work has been carried out in rapidly developing countries such as India and China where urban development is dynamic and not always easy to measure. In countries with less rapid urbanization, such as the United States, there is still value in using satellite imagery to estimate building energy use for the purposes of identifying energy efficiency opportunities and planning electricity transmission. This study evaluates nighttime light imagery obtained from the VIIRS instrument aboard the SUOMI NPP satellite as a predictor of building energy use intensity within states, counties, and cities in the United States. It is found that nighttime lights can explain upwards of 90% of the variability in energy consumption in the United States, depending on conditions and geospatial scale. The results of this research are used to generate electricity and fuel consumption maps of the United States with a resolution of less than 200 square meters. The methodologies undertaken in this study can be replicated globally to create more opportunities for geospatial energy analysis without the hurdles often associated with disaggregated building energy use data collection.

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Optimal Design for Carbon Neutral Communities
This study investigates the optimal design and retrofit solutions for residential communities to be carbon neutral. The optimization-based design and retrofit approach involves the use of a bottom-up modeling technique for the individual buildings and life cycle cost analysis. The approach has been applied to various communities throughout the world. For the case of existing residential communities in Barcelona Spain, the best cost-effective energy efficiency measures (EEMs) are determined for representative buildings within the communities to reduce their overall energy demand. The retrofit optimization results demonstrate that combinations of passive and active strategies would achieve source energy use saving and electrical peak demand savings up to 57 percent��and 29 percent, respectively. Then, a variety of renewable energy generation systems has been explored to determine the optimal grid-connected technologies for meeting the electricity demands while achieving annual carbon neutral conditions for the residential buildings. The analysis shows that wind turbines can reduce the cost of electricity generation up to 21.5 percent��compared to the current utility prices (LCOE value as 0.247 €/kWh) and electricity sell back policies. The results indicate that for PV systems to compete with wind turbines, their capital costs need to be lower by at least 40 percent��compared to the current level costs.

Building Energy Productivity Analysis Framework
In this project, a new analysis framework is developed and applied to assess the impact and the benefits of building energy efficiency policies and programs. The analysis framework considers both energy and non-energy benefits of building energy efficiency both at the macro and the micro economic scales. Specifically, the approach utilizes the concept of energy productivity of the building sector and accounts for both value added and energy savings of energy efficiency measures. The proposed analysis accounts for all quantifiable benefits of energy efficiency programs including economic, environmental, and social impacts. For demonstration, the energy productivity analysis framework is applied to evaluate large scale energy efficiency programs for both existing and new buildings in the Gulf Cooperative Council (GCC) countries. It is found that retrofitting the existing building stock can provide significant benefits and can improve the energy productivity of the building sector in all GCC countries. In particular, the analysis presented in this paper indicates that reduction in energy consumption, peak demand, and carbon emissions due to a level 3 retrofit programs for the existing building stock can double the energy productivity of the GCC region.

Mitigation Analysis of Water Consumption for Power Generation and Air Conditioning
This study describes an analysis framework to assess water consumption attributed to electricity generation required to meet the demand for building stocks. In addition, the analysis aims at estimating the water consumption reduction due to cost-effective energy retrofit measures for the building stock. The analysis framework has been applied to existing housing stock for Saudi Arabia. The analysis estimated that the water consumed annually to generate electricity for the Saudi entire housing stock is 135 MCM representing almost 10 percent��and 4 percent of the water used by the industrial sector. Moreover, it is found that both electricity generation needs, and associated water demands can be reduced by 15.7 percent��when lighting is retrofitted with low-energy fixtures and by 25.8 percent��when high efficiency air conditioning systems are installed for all the existing Saudi housing stocks.�� For the housing stock located in the Central region with prevalent dry climates, replacing existing air conditioning by evaporative coolers can save 11.1 TWh/a (25.5 percent) in electricity consumption but increase the water consumption by 36.2 MCM/a (80.6 percent). A cost-benefit analysis of lighting retrofit is found to be highly cost-effective for both households and the government with payback periods of less than one��year.

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