Medium-sized Commercial Buildings Research Articles

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.

Deploying data driven applications in smart buildings: Overcoming the initial onboarding barrier using machine learning

The introduction of smart building technology promises many operational and productivity benefits and enables smart grid integration. A significant barrier to deploying smart building applications is mapping the building sensor metadata to the requirements of the smart building applications. In this paper, we have studied the problem of weakly supervised machine learning to accelerate the metadata mapping process, and demonstrated that weak supervision is a promising approach, which to the best of our knowledge, has not been studied in this context. We developed a pattern-based workflow that enables subject matter experts to craft simple rules that process the idiosyncratic text found in building sensor metadata. Our method was validated using three medium-sized commercial office buildings and a public dataset. We worked closely with a team of commercial building subject matter experts to develop and validate our workflow. We compared our approach to active learning and discovered that weak supervision appears to scale better with building size. Subject matter experts overwhelmingly preferred this approach compared to manual methods, and our study with three buildings shows that weak supervision can reduce the time to annotate the metadata in building systems by a factor of 4.

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.

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.

Estimating ASHRAE Guideline 36 energy savings for multi-zone variable air volume systems using Spawn of EnergyPlus

ASHRAE Guideline 36 (G36) publishes high-performance control sequences for Variable Air Volume (VAV) system operation. Retrofitting existing VAV control sequences to G36 promises to have a large potential for energy savings. However, it is difficult to estimate the savings accurately and the process of doing so can be costly and time-consuming. This paper evaluates the energy use of a multi-zone VAV system with terminal reheat using the G36 sequences and compares it to a group of baseline control sequences that represent existing practices. Spawn of EnergyPlus is used for the whole building simulation, where the envelope is modelled in EnergyPlus and the HVAC equipment and its pressure-flow network and the control sequences are modelled in Modelica. The comparison of the control sequences performance is further conducted in parametric studies. For a medium-sized commercial building, the G36 sequences provide a wide range of HVAC energy savings with an average of 31%.

Standardizing Performance Metrics for Building-Level Electrical Distribution Systems

Building-level electrical distribution systems comprise a myriad of current-carrying equipment, conversion devices, and protection devices that deliver power from the utility or local distributed energy resources to end-use building loads. Electric power has traditionally been generated, transmitted, and distributed in alternating current (AC). However, the last decade has seen a significant increase in the integration of native direct current (DC) equipment that has elevated the importance of DC distribution systems. Numerous studies have comparatively examined the performance of various electrical distribution systems in buildings but have failed to achieve uniform conclusions, primarily because of a lack of consistent and analogous performance evaluation methods. This paper aims to fill this gap by providing a standard set of metrics and measurement boundaries to consistently evaluate the performance of AC, DC, or hybrid AC/DC electrical distribution systems. The efficacy of the proposed approach is evaluated on a representative medium-sized commercial office building model with AC distribution and an equivalent hybrid AC/DC and DC distribution model, wherein the AC distribution model is concluded to be the most efficient. The simulation results show variation in computed metrics with different selected boundaries that verify the effectiveness of the proposed approach in ensuring consistent computation of the performance of building-level electrical distribution systems. This paper provides an initial set of guidelines for building energy system stakeholders to adopt appropriate solutions, thus leading to more efficient energy systems.

상업·공공용 건물에너지의 지역별 소비 특성을 반영한 GCAM-Korea 모형 개선

Information on provincial level energy consumption pattern of commercial and public buildings, which account for about 49% of domestic building energy as of 2016, is not available yet. In this study, the analysis of provincial level energy consumption characteristics of overall commercial and public buildings is conducted, including small and medium-sized commercial and public buildings. Provincial level building energy consumption pattern is derived, utilizing 2017 Energy Consumption Survey for Commercial and public buildings conducted by KEEI (Korea Energy Economics Institute), presented in aggregated form by sector, fuel, and energy use type, without regional information, and 2017 population census on building establishments conducted by National Statistics Office, Korea. This disaggregated information resulting from this analysis is applied to GCAM-Korea (Global Change Assessment Model-Korea) for the better representation of the details of commercial and public buildings energy characteristics. As the result, the data show different energy consumption characteristics at provincial level. It is found that existing GCAM-Korea underestimates cooling and heating electricity energy while it overestimates other use types of electricity for commercial and public buildings at all regions. The results of this study shed light on regional energy plan for commercial and public buildings.

A screening method for lowering customer acquisition cost in small commercial building energy efficiency projects

The retrofit of buildings to improve energy efficiency represents a vast opportunity in the construction market with the potential to reduce energy demand, create jobs, and achieve environmental benefits. Multiple challenges need to be overcome to unleash this potential and realize the benefits of investments in building energy retrofit projects. Small-/medium-sized commercial buildings are typically underserved by the energy retrofit industry due to the lack of owners’ ability to manage and finance energy efficiency improvements. In the case of energy-intensive and low-margin types of commercial buildings such as restaurants, the potential for energy savings is high, but the favorable conditions that enable retrofits, such as a pending renovation, propensity of clients to conserve energy, and willingness to take time for a retrofit, are scarce. In this research, the development of small energy efficiency projects is examined by proposing a screening method to find prospective candidates for financed energy efficiency projects in a targeted region. This method enables the co-creators of energy retrofit projects to predict the decision-making behavior of business owners and their willingness to invest in energy retrofit based on their restaurants’ attributes in social media. As part of a current initiative, supported by the Pennsylvania Department of Environmental Protection, a series of design, development, and delivery of retrofit projects in the context of food services was closely studied. The initial focus was on the Philadelphia region as a model for broader statewide applications. Restaurant owners were surveyed, and information was collected about their buildings’ energy systems, utility bills, and their willingness and/or capacity to invest in energy efficiency upgrades. A “Philadelphia restaurants” dataset was then scraped from the Yelp website. The data includes information about restaurant ratings, number of reviewers, price range, working hours, and ambiance. A methodology was developed to analyze the data and to find patterns and criteria that could possibly influence the owner’s decision for investing in energy performance improvement. To identify patterns, two methods were employed: (1) several focus groups over program period with team members who played roles in project development phase and (2) statistical analysis to identify the factors contributing to successful projects. Using these screening questions from focus groups and the results from statistical analysis, the retrofit likelihood can be estimated for each individual project. This methodology can be applied to larger datasets and would enable the investigators to screen the whole market and prioritize the targets for business recruitment.

Factors influencing existing medium-sized commercial building energy retrofits to achieve the net zero energy goal in the United States

ABSTRACT Despite the increased awareness of benefits and opportunities resulting from energy retrofits, there is a lack of information regarding the factors influencing the success of energy retrofits – especially deep energy retrofits – aiming to achieve a net zero energy goal. The aim of this study is to identify factors and variables that could contribute to the successful implementation of energy retrofit measures in buildings with the goal of achieving net zero energy. A database of 34 small- and medium-sized commercial building retrofit projects was developed for this analysis, which was derived from the New Buildings Institute’s Net Zero Energy Building Database. Five factor categories were investigated: physical, technical, environmental, economic, and policy. A total of 11 variables from the 5 categories were analysed. The results show that success stems from a combination of certain variables: the number of floors, the climate zone, and the building cost. In general, the results indicate a trend: compact buildings with a construction cost range between $19/m2 and $42/m2 in a mild climate have a higher likelihood of achieving a net zero energy goal. Also, the analysis provides new evidence toward the importance of understanding a building’s physical characteristics in an existing building energy retrofit.

Sizing and dispatch of an islanded microgrid with energy flexible buildings

Deregulation of the electricity sector, the rise of distributed generation, and a growing interest in local resilience have led to increasing attention on microgrids. In this paper, we present an approach for sizing the microgrid components that accounts for the load flexibility available in buildings with model predictive control. As buildings are becoming smarter, the use of building control systems to regulate building load for different strategies, such as peak demand limiting or load shifting, is becoming increasingly prevalent. When sizing microgrid components under islanded operation, it becomes critical to consider the dynamic nature of the building load, since the intelligent control systems can use the building response to help balance energy flows. An optimal sizing and dispatch model of the microgrid with model predictive control is developed. Simulations are carried out for representative days for a building-level microgrid serving a medium-sized commercial building. Results show that savings in first costs and operational costs can be realized if advanced controls are considered during design and component selection.

Optimal control parameter for electrochromic glazing operation in commercial buildings under different climatic conditions

Electrochromic glazing can dynamically control solar heat gains of windows depending on the indoor and outdoor environments. This means that the energy performance of buildings with the electrochromic glazing window systems are affected by control parameters and climatic conditions. This study proposes an optimized electrochromic glazing control parameter and value to improve the energy performance and sustainability of medium-sized commercial building in different climates. It characterized the annual heating and cooling energy consumption of a typical building model with the window systems for different potential control parameters, such as outdoor air temperature, room air temperature, solar radiation incident on the window, and global horizontal solar irradiance. A pattern search algorithm was applied to derive the optimal control value of each parameter for four window orientations in six different climatic conditions. Our results show that for the electrochromic glazing window operation, outdoor air temperature is the most effective control parameter for reducing both cooling and heating energies in all window orientations and climatic conditions. When the electrochromic glazing window on three sides of building is controlled at the optimal outdoor air temperature, the cooling coil size of the air handling unit decreased by 16.5% on average under different climatic conditions. Furthermore, the daily peak cooling loads reduced by an average of 11.8%. The annual heating and cooling energy consumption decreased by 46.1kWh (17.4%) on average per unit window area compared with the typical static window case under six different locations.

A Study on the Price Determinants of Small and Medium-Size Commercial Buildings in Ulsan Metropolitan City

A Study on the Price Determinants of Small and Medium-Size Commercial Buildings in Ulsan Metropolitan City

Development and evaluation of HVAC operation schedule detection algorithm

Small- and medium-sized (<5,000 m2 or <50,000 sf) commercial buildings (SMBs) represent over 94% of the U.S. commercial building stock and consume over 1018 kJ (∼1015 Btus) of total site energy. Many of these buildings use rudimentary controls that are mostly manual, with limited scheduling capability and no monitoring or failure management. Therefore, many of the systems in these buildings are operated inefficiently and consume excess energy. SMBs typically use packaged rooftop units (RTUs) that are controlled by an individual thermostat. Studies have shown that managing set points and schedules of the RTUs will result in up to 20% energy/cost savings. Because there is no monitoring infrastructure in these buildings, deviation of actual heating, ventilation and air conditioning (HVAC) set points and schedules from expected cannot be easily inferred or detected. In this paper, a new methodology is proposed to detect HVAC operation schedules using a symbolic aggregate approximation (SAX) technique. This new method only requires a single temperature measurement (zone temperature) to infer the HVAC schedule. The method was tested and validated using field data from a number of RTUs from six buildings in different climate locations. The confusion metric was used to evaluate how well the method is able to identify the operating schedule of RTUs. Overall, the schedule detection method was successful in detecting the daily operating schedule of RTUs under summer (hot) and winter (cold) weather conditions when energy use is highest or when the RTUs are cycling ON/OFF often. The method can also distinguish different schedules and generate clusters of HVAC operating schedules.

Development, implementation and performance of a model predictive controller for packaged air conditioners in small and medium-sized commercial building applications

Small and medium sized commercial buildings, such as retail stores, restaurants and factories, often utilize multiple packaged air conditioners, i.e. roof top units (RTUs), to provide cooling and heating for open spaces. A conventional control approach for these buildings relies on local feedback control, where each unit is cycled on and off using its own thermostat. The lack of coordination between RTUs represents a missed opportunity for operating more efficient units when there is strong coupling between the spaces they serve and can lead to unnecessarily high electrical demand due to the inherent randomness of unit cycling. This paper presents an overall model-based predictive control (MPC) approach for RTU coordination that includes a description of the control architecture, modeling approach, implementation, and assessment. We provide results of laboratory and field tests that demonstrate the short-term and long-term performance of the MPC solution in terms of energy and demand savings.

Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid

Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.

A new simplified modeling method for model predictive control in a medium-sized commercial building: A case study

Model predictive control (MPC) methods for heating, ventilation and air conditioning (HVAC) systems have been studied to improve the control accuracy and reduce energy consumption in recent years. The accuracy of the model for building thermal dynamics in MPC plays a critical role to accurately control the system. The modeling method also impacts on the real practice of MPC in buildings due to its cost and scalability. Studies have shown that an appropriate simplification of modeling procedure has minor impacts on the model accuracy, but increases the modeling efficiency. In this article, variables including weather conditions, occupancy and electricity are divided into two categories: manipulated variables and random variables. A novel two-step modeling strategy is proposed for simplifying modeling procedure and increasing model accuracy. Manipulated variables are used in step response method to develop system model. A low order system is obtained after the model simplification by observing the response curve. Random variables are used in the power spectral density (PSD) method for modeling. Transfer function is obtained through calculating the cross-power spectral density (CPSD) of the system output and input, the PSD of the input, and the ratio of CPSD and PSD. A MPC strategy with feedforward control structure is proposed to utilize the obtained dynamic characteristics of random variables and effectively compensate the errors caused by these variables. Field test in a medium-sized commercial building is implemented to evaluate the MPC strategy. The result shows that a considerable amount of energy saving is achieved through the proposed MPC.

A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings

With the advent of the smart grid, demand response (DR) has been implemented in many electric utility control areas to reduce peak demand in buildings during grid stress conditions. However, small- and medium-sized commercial buildings typically do not deploy a building energy management (BEM) system due to high costs of commercially available solutions. Thus, their participation in DR events implies manual control and shutting down major building loads (e.g., air conditioning systems) without considering occupant comfort. With rapid development of Internet of Things (IoT) technologies, some cost-effective IoT-based BEM systems have become available. Based on such systems, this paper presents an algorithm to automatically coordinate the operation of rooftop units (RTUs) in small- and medium-sized commercial buildings, thereby meeting the specified power limit (kW) during a DR event while taking into account occupant comfort. The proposed algorithm has been designed to intelligently learn building thermal properties using coarse-grained indoor temperature data from thermostats, thus avoiding the deployment of sophisticated sensors network. A mixed-integer linear programming model has been utilized to determine an optimal RTU control strategy during a DR event. The peak load shedding performance of the proposed strategy has been tested in an office building in Blacksburg, VA, USA. The experimental result demonstrates that the building could achieve the required peak load reduction and the computation time required by the proposed algorithm is less than 5min. This implies that with the proposed algorithm a building is capable of responding to a DR signal with a short notice, providing valuable demand-side resources for electricity capacity and ancillary markets.

Electric load shape benchmarking for small- and medium-sized commercial buildings

Small- and medium-sized commercial buildings owners and utility managers often look for opportunities for energy cost savings through energy efficiency and energy waste minimization. However, they currently lack easy access to low-cost tools that help interpret the massive amount of data needed to improve understanding of their energy use behaviors. Benchmarking is one of the techniques used in energy audits to identify which buildings are priorities for an energy analysis. Traditional energy performance indicators, such as the energy use intensity (annual energy per unit of floor area), consider only the total annual energy consumption, lacking consideration of the fluctuation of energy use behavior over time, which reveals the time of use information and represents distinct energy use behaviors during different time spans. To fill the gap, this study developed a general statistical method using 24-h electric load shape benchmarking to compare a building or business/tenant space against peers. Specifically, the study developed new forms of benchmarking metrics and data analysis methods to infer the energy performance of a building based on its load shape. We first performed a data experiment with collected smart meter data using over 2000small- and medium-sized businesses in California. We then conducted a cluster analysis of the source data, and determined and interpreted the load shape features and parameters with peer group analysis. Finally, we implemented the load shape benchmarking feature in an open-access web-based toolkit (the Commercial Building Energy Saver) to provide straightforward and practical recommendations to users. The analysis techniques were generic and flexible for future datasets of other building types and in other utility territories.

Improving HVAC operational efficiency in small-and medium-size commercial buildings

Studies have shown that managing the setpoints and schedules of rooftop units (RTUs; air-conditioners and heat pumps) will result in up to 20% energy and cost savings. Another problem associated with RTUs is short cycling, i.e., when an RTU goes through ON and OFF cycles too frequently. Excessive cycling can result in decreased average efficiency (up to 10%), even if there are no physical failures in the equipment. Many small- and medium-size commercial buildings (SMBs) use rudimentary controls that are mostly manual and have limited scheduling capability and no monitoring or failure management. Therefore, many of these buildings are operated inefficiently and unnecessarily consume excess energy. SMBs typically use packaged RTUs that are controlled by individual thermostats. Ensuring the correct use of the zone setpoint and eliminating too frequent cycling of RTUs, thereby leading to persistent building operations, can significantly increase the operational efficiency of the SMBs. The work reported in this paper describes two algorithms for detecting the zone setpoint temperature and RTU cycling rate that can be deployed on low-cost infrastructure. These algorithms only require zone temperature data for detection without any additional sensor installation or supervised learning. The algorithms have been tested and validated using field data from 24 RTUs from six buildings in different climate locations. Overall, the algorithms were successful in accurately detecting the setpoints and ON/OFF cycles.

Building energy model calibration with schedules derived from electricity use data

Building energy models can accurately predict energy performance of buildings, if properly calibrated. This study developed and demonstrated a novel method to calibrate building energy models based on the occupancy and plug-load schedules derived from metered electric use data. Importantly, this study also proposed an occupancy assessment method applicable to resource limited situation when a building sub-metering system is not available. Furthermore, the developed method can facilitate accurate predictions of building energy performance without a requirement to simultaneously monitor energy use and occupancy rates. The method development process used data from an office type building (OB1), and further verified the method accuracy with data from two campus buildings (CB1 and CB2). The developed method is novel because it considers interactions of the validated modeled occupancy patterns, processed electricity use patterns, and the calibrated building energy model results at the hourly level. This approach allows addressing limitations in the current studies that are not fully capable of modeling occupancy patterns, electricity use patterns, and calibrated building energy models with this level of granularity. The accuracy of the building energy modeling results increases with the derived occupancy schedules and plug-loads. Specifically, the Coefficient of Variation Root Mean Square Error (CVRMSE) of OB1 building energy modeling results improved from 21% to 12% compared to the modeling results obtained with default schedules. The results from case study buildings CB1 and CB2 show that the accuracy of modeling results increased as the hourly electricity CVRMSE decreased from 128% to 31% and from 156% to 16%, respectively. These improvements are significant, while the developed method is applicable to other office or campus buildings from the category of medium-size commercial buildings. Finally, the identification of actual occupancy rates provides opportunities for inexpensive implementation of occupant-based controllers in buildings.