Small Commercial Buildings Research Articles

How to cool a warming world? – The potential of photovoltaic green cooling with natural refrigerants in sunbelt countries

The study investigates the economic feasibility of photovoltaic-powered air-conditioning (AC) systems in thirteen different sunbelt countries. Two different technical solutions have been analysed: (i) hybrid (partially PV powered, grid-connected) and (ii) off-grid (fully PV powered, no grid connection). These two solutions have been studied for three different locations in each country, namely minimum, average and maximum annual global solar irradiation on the horizontal. Lastly, each solution and location has been examined for application in the residential and commercial sector. All solar-based air-conditioning scenarios use high efficiency AC appliances with R290 as refrigerant (Global Warming Potential – GWP of 1) and have been compared against a base-case scenario using AC units with moderate efficiency AC appliances with conventional R410a refrigerant (GWP of 2088) and 100 % grid electricity supply. The scope of the study is to identify the economic potential of solar PV cooling technologies. Levelized Cost of Electricity (LCOE) and Net Present Value (NPV) have been calculated for each scenario as part of the comparative analysis. It was found that hybrid photovoltaic-based air-conditioning for a residential house has a financial advantage over grid-based air-conditioning in eleven out of thirteen countries investigated. Exceptions are Ghana and Vietnam. Off-grid photovoltaic-based air-conditioning for residential houses is only economically feasible in five out of thirteen countries, namely Costa Rica, Iran, Grenada, Philippines and Thailand. Hybrid photovoltaic-based air-conditioning for a small commercial building has a financial advantage over grid-based air-conditioning in ten of thirteen countries, except in Colombia, Ghana and Iran. Off-grid photovoltaic-based air-conditioning for a small commercial building is only feasible in China, Grenada, Kenya and Philippines.

Implementation and validation of optimal start control strategy for air conditioners and heat pumps

Commercial buildings are responsible for approximately 20 % of the total energy consumption and greenhouse gas emissions in the United States. Over 85 % of these buildings lack building automation systems, and many are small (<50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, several operational deficiencies lead to excess energy consumption. Studies have shown that managing the RTUs’ heating and cooling set points, schedules, setbacks, and optimal start can result in a 20 % to 25 % reduction in electricity consumption in small commercial buildings. These buildings typically use fixed schedules to start the RTUs 60 to 120 min before occupancy begins, which results in excess energy consumption. This paper presents research that demonstrates and evaluates the performance of four optimal start methods, which utilize data-based modeling as a key element in facilitating adaptive control in response to time-varying inputs while requiring minimal sensor inputs. The evaluation found energy savings in two commercial buildings equipped with RTUs by periodically alternating four different optimal start models during the cooling and heating season. The resulting energy savings are positive for all models and range from 2 to 5 kWh/day/unit. The units on the east side of the building showed higher savings, while interior units showed greater variability in savings due to the differences in capacities and room sizes. Savings were considerably greater during the heating season compared to the cooling season. The performance of all four models on Mondays was poor; models suggested a shorter optimal start time, which resulted in relatively larger errors. The future work will look at using a different model for the days after weekends and holidays.

Evaluation of data imputation approaches for multi-stream building systems data1

Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and controlling building systems. However, due to sensor or communication failures, the data collected are often incomplete and poor in quality. Data imputation approaches to replace the missing values, specifically based on either statistical or predictive models have been widely adopted for multivariate datasets in other domains. It is hence of interest to find an effective way to impute timeseries data collected from a building system. In this paper, we evaluate multiple data imputation approaches using data collected from a medium sized building situated in Stockholm, Sweden and a small commercial building from the ASHRAE RP-1312 research project. Sensors with widely varying characteristics from the case study buildings were selected to evaluate the imputation methods. The imputation accuracy and the impact of each chosen imputation method on information entropy, short-term building forecasting model performance, and fault detection strategy were evaluated. Results demonstrate that incorporating time-lagged cross correlations within a k -nearest neighbor ( k NN) model provide the most accurate imputations without affecting the quality of subsequent data analysis.

Finite element analysis of fibre reinforced timber beams under flexural loading

ABSTRACT Laminated Veneer Lumber (LVL) is a popular product used in the building and construction industry because of its high strength, serviceability, aesthetic characteristics, and cost-effectiveness. However, LVL has traditionally been limited to residential and small light commercial buildings. To expand the use of LVL and reduce the need for steel and reinforced concrete structural members, a comprehensive computational study is conducted to understand the material’s structural performance and find an optimal strengthening ratio. In this paper, a new 3-dimensional Finite Element model was introduced and evaluated to predict the structural behaviour, strength properties, and failure damage modes of LVL timber beams reinforced with a Carbon Fibre Reinforcement Polymer (CFRP) sheet. The model is validated with three experimental tests from the literature, with the reinforced timber model showing a 1.8% error compared to the plain timber model’s 7% error. Additionally, a parametric study is conducted to understand the effects of reinforcement length on the structural behaviour of the reinforced beam, with mathematical equations established to express the relationships. The study found that reinforcing 50% of the beam span generally resulted in the most significant strength improvement. This investigation is made for various strength and serviceability parameters.

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environement, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. . The results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

Empowering Owner-Operators of Small and Medium Commercial Buildings to Identify Energy Retrofit Opportunities

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings’ owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves.

Using smart thermostat override data to provide insights for improving heating, ventilation, and air-conditioning system scheduling in a portfolio of small commercial buildings

Managers of small commercial building (SCB) portfolios need to understand occupant interactions with heating, ventilation, and air-conditioning (HVAC) systems to reduce energy use and greenhouse gas (GHG) emissions. In Canada, SCBs are currently underserved by energy conservation and thermal analysis tools because of their dispersion and lower payback potential. However, the emergence of smart thermostats (STs) and their central data collection platform provide a cost-effective solution to gather data from portfolios of SCBs and improve our understanding of occupant-HVAC interactions. This article analyzes the relationship between HVAC schedules (temperature set-points), indoor thermal conditions (dry-bulb temperature and relative humidity), and occupant behavior (thermostat overrides) in a portfolio of 30 SCBs in Ontario, Canada. The results reveal that temperature set-points were not properly selected in the portfolio of SCBs, leading to a large range of indoor thermal conditions and increased thermostat overrides. Specifically, the study demonstrates that building- and zone-specific HVAC schedules are necessary to minimize discomfort and reduce energy consumption in the portfolio of SCBs. The findings of this study can provide valuable insights for portfolio managers to improve HVAC schedules in a manner that reduces energy consumption and GHG emissions while accommodating occupants’ thermal comfort and productivity.

Modelling variable refrigerant flow system for control purpose

Variable Refrigerant Flow (VRF) systems are gradually gaining popularity in small and medium-sized commercial and residential buildings owing to their high part-load performance, flexible control, and ease of installation and maintenance. Developing models of VRF systems to predict their performance are important for model-based control, fault diagnostic and detection. There are VRF models published in existing literatures, however those models were developed and validated in different datasets. As a result, the model accuracy cannot be directly compared. To fill this gap, this paper presents a comprehensive review of the existing VRF models, and summarizes the input/output parameters and mathematical formulas of 16 VRF models from literature (referred to as physics-based model). Next, we validate and compare the model accuracy of existing models using the same dataset. Additionally, we develop data-driven models using the state-of-art machine learning algorithms, and compare the model accuracy between existing physics-based models with data-driven models. We find the model proposed by Hu et al. in 2019, which regresses the VRF cooling capacity and COP as a linear combination of indoor and outdoor temperatures times a cubed polynomial function of compressor frequency, is the most accurate physics-based model, with a prediction error of 22.19% in the training dataset and 22.44% in the validation dataset. XGBoost is the most accurate data-driven model, with a prediction error of 19.29% in the training dataset and 22.02% in the validation dataset. The data-driven model is more accurate while the physics-based model is more generalizable. The findings of this study can help researchers to select the proper VRF model for building energy prediction, model-based optimization, and fault diagnostic and detection.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief network (DBN) classification model, which identifies seven distinct types of faults. At the same time, the chicken swarm optimization (CSO) algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process, showing the novelty of the work. To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm, a comprehensive set of simulations are applied. The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30% and TPR of 93.09%.

소규모 상가 건물의 환기량을 고려한 냉방시스템 적정 용량 제시를 위한 분석

In this study, cooling loads of small commercial buildings were calculated by applying the ventilation rate according to the Indoor Air Quality Control Act in order to suggest the appropriate cooling system capacity. The analysis model followed the latest standards and forms considering the use characteristics, and thus the ceiling height was raised and the window to wall ratio (WWR) was increased compared to the KS C 9306. In addition, KS C 9306 applied the CLTD method, but in this study, the RTS method, which is currently widely used in the design of offices, was applied. In general, the WWR of commercial spaces tends to be high and the cooling load is greatly affected by solar radiation. The cooling load does not considerably decrease due to the improvement of the insulation performance of the envelope. However, the ventilation rate changes have a relatively large impact on the cooling load. If small commercial buildings comply with the

Advanced smart trigeneration energy system design for commercial building applications – Energy and cost performance analyses

Smart building-integrated trigeneration technology has the potential to meet the world goals for significant reduction of greenhouse gas (GHG) emissions. Some of the new research and development policies are aimed at development of novel and renewable hybrid systems, shift from unit to integration technology; implementation of demand-side management; move from centralized macro to decentralized micro technologies, etc. Recognizing the importance of trigeneration in future energy mix, hybrid renewable trigeneration systems - with integration of photovoltaic-thermal (PVT), ground source heat pump (GSHP) and smart control - were developed for a small commercial building. This paper discusses the energy and cost performance as well as GHG emissions of these systems sized to meet building space heating/cooling loads and HVAC electric power requirements. The results enhance the knowledge and provide useful information to designers on how these trigeneration systems should be applied under variety of conditions, what are their advantages in comparison to a single ground source heat pump system, and what are the energy, economic and GHG emission impacts of smart communications and artificial intelligence controls.

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

Real time side-by-side experimental validation of energy and comfort performance of a zero net energy retrofit package for small commercial buildings

Making buildings zero-net energy (ZNE) is one of the major strategies for achieving carbon emission reduction goals. For this strategy to be successful, it entails a very significant reduction in energy use – 50% or more. In small commercial buildings, however, owners and building management teams usually have limited resources for identifying, analyzing, and procuring appropriate retrofit measures for reducing such use. An approach to overcome this limitation is the development of bundles of energy efficiency measures that can be presented to building owners/operators as a comprehensive package. The research presented in this paper focuses on an experimental evaluation of the impacts of a retrofit package developed for small office buildings in California. Performing this evaluation in a full-scale whole-building integrated systems test facility allowed a side-by-side evaluation in real time, against a reference case, of the impact of the retrofit package not only on energy use but also on visual and thermal comfort. The retrofit package evaluated is comprised of a combination of HVAC, lighting (including daylighting), and plug load measures. The evaluation occurred at different times of the year in order to account for seasonal variations in environmental conditions, including solar angles and weather. The experimental facility allowed testing for two different façade orientations: south and west. Results show that the proposed ZNE retrofit package can save significant amounts of energy for small commercial buildings. During cooling-prevalent periods, total energy savings were 65% for south orientation and 68% for west orientation; during heating-prevalent periods total energy savings were 22% for south orientation and 25% for west orientation. Measurements indicate that the ZNE retrofit package resulted in small but not very significant changes in comfort levels for building occupants.

MPC solution for optimal load shifting for buildings with ON/OFF staged packaged units: Experimental demonstration, and lessons learned

Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. Two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.

Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters

Deployment of CTA-2045–enabled devices is increasing in the U.S. market. These devices allow utilities or third-party aggregators to control appliance energy use in homes, and could also be applied to end uses in small commercial buildings. This study focuses on a field study using CTA-2045–enabled water heaters to shift electric load off the peak and toward periods when renewable resources are more prevalent (e.g., near noon for solar resources and near midnight for wind resources). The following load shifting strategies were compared to understand effects on the aggregate load-shifting capabilities of Heat Pump Water Heaters (HPWHs) and on consumer hot water supply: non-targeted (traditional), targeted (grouped, with different shifting schedules) and “smart” (adaptive control commands). The results of this study show that targeted and smart control strategies yield significantly more load-shifting potential from a population of water heaters than the non-targeted approach without sacrificing hot water supply to occupants. However, as control commands become more aggressive, aggregators may face challenges in meeting consumer hot water demand. The findings and lessons learned can benefit electric utilities and inform updates to manufacturer controls and communications standards. The data collected may also be useful for developing and validating HPWH models.

Energy Savings Results from Small Commercial Building Retrofits in the US

Small commercial buildings, or those comprising less than 50,000 square feet of floor area, represent 94% of U.S commercial buildings by count and consume approximately 8% of the nation’s primary energy; as such, they represent a largely unexploited opportunity for energy savings. Small commercial buildings also represent a large economic market—the National Institute of Building Sciences (NIBS) estimated the small commercial retrofit market at USD 35.6 billion. Despite the prominence of small commercial buildings and the economic opportunity for energy retrofits, many energy efficiency programs focus on large commercial buildings, and create efficiency solutions that do not meet the needs of the small commercial market. This paper presents an analysis of 34 small commercial case study projects that implemented energy efficiency retrofits. This paper contributes to the existing building retrofit body of knowledge in the following ways: (1) it identifies the decision criteria used by small commercial building stakeholders that decided to complete an energy retrofit; (2) it identifies the most commonly implemented efficiency measures in small commercial buildings, and discusses why this is the case; and (3) it provides empirical evidence about the efficacy of installing single energy efficiency measures (EEMs) compared to packages of EEMs in small commercial buildings by reporting verified energy savings. To the authors’ knowledge, this paper is the first to catalog decision criteria and energy savings for the existing small commercial buildings market, and this research illustrates that small commercial building decision-makers seem most motivated to retrofit their spaces by energy cost savings and operational concerns. Furthermore, small commercial building decision-makers opted to implement single-system retrofits in fifteen (15) of the thirty-four cases studied. Finally, this research documents the improved savings, in the small commercial buildings market, associated with a more integrated package of EEMs compared to a single-system approach, achieving approximately 10% energy savings for a single-system approach and more than 20% energy savings for integrated approaches. These savings translate to CO2 savings of 1,324,000 kgCO2/year to 2,647,000 kgCO2/year, respectively, assuming small commercial buildings are retrofit at a rate of 0.95% of the stock annually.

K-Nearest patterns for electrical demand forecasting in residential and small commercial buildings

This work presents a case study of Big Data and Machine Learning whose objective is to improve energy Demand Response (DR) programs by providing accurate energy demand forecasts. Given the present state of the art, this research work introduces the proposed methodology for Time Series Forecasting based on two variants of the K-neighbours method (KNN): K-Nearest Features in Time Series (KNFTS) and K-Nearest Patterns in Time Series (KNPTS) algorithms. These algorithms are valuable in this field since only a historical data set consisting the time and energy consumption variables are used to find similar patterns of electricity consumption and then make future forecasts. Furthermore, the proposal to use elastic similarity measures such as DTW and EDR shows to have advantages over the use of common error metrics. It has been proven on data from 122 houses and small commercial buildings located on the island of Lanzarote that the KNPTS achieves minor errors in 89% of cases. Therefore, it shows that the KNPTS algorithm provides a good accuracy, more efficient in prediction than the KNFTS algorithm, to improve DR programs.

Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings

Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.

A review of Stirling-engine-based combined heat and power technology

Small- and micro-scale combined heat and power (CHP) technologies offer great potential for reducing energy costs and CO2 emissions in residential and small commercial buildings. Among various CHP technologies, Stirling engines, particularly free-piston ones, show great promise in residential applications because of their remarkable advantages of low emissions, maintenance, noise, and vibration, theoretically high thermal-to-electrical efficiency, and flexibility of fuel sources. This paper presents a comprehensive review of Stirling-engine CHP in terms of different heat sources, tri-generation systems, status of commercial development, techno-economic issues, and challenges and future trends. The techno-economic assessment indicates that the relatively low on-site operational efficiencies and considerable investment costs are the main issues that hinder the further development of Stirling-engine CHP systems, and suggestions for future development are made accordingly. For successful commercial dissemination of Stirling-engine CHP, it is essential to improve the on-site electrical efficiency, reduce the capital cost of systems, and develop renewable-energy-powered and free-piston Stirling-engine-based CHP systems against the background of grid decarbonization and carbon neutrality.

On the optimization of energy efficient fenestration for small commercial buildings in the United States

Currently, commercial buildings consume almost 18% of the total primary energy in the US and this is bound to increase with time due to population and economic growth. Small commercial buildings are those with less than 50,000 ft2 of conditioned area and represent 94% of total commercial buildings in the U.S. Since windows are responsible for 34% of commercial space conditioning energy use, windows in small commercial buildings should be given considerable attention. Besides the window material, selecting an optimum window dimension and location has played an important role in building energy performance. Since different weather conditions and sun angles impact on determining the optimum window dimensions and location, the goal of this study is to identify the optimum window design parameters in seven US climate zones in order to minimize building energy use.In this study, an optimization model using a genetic algorithm is developed and coupled with EnergyPlus software to identify the optimum window design parameters including a window to wall ratio, aspect ratio, and fenestration location. A double-story commercial building model, as a case study, is designed using DesignBuilder software to illustrate the application of the model. Identifying the optimum window dimensions and location in different locations would help engineers and designers to reduce building energy consumption in the early stages of the design. The results show that selecting optimum window dimensions and locations can reduce the total building energy consumption by 2 and 15% in cold and hot climate zones respectively.