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The Cost-Effectiveness of Commissioning New and Existing Commercial Buildings: Lessons from 224 Buildings
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Synopsis Scattered case studies and anecdotal information form the "conventional wisdom" that building commissioning is highly cost-effective. Given that this belief has not been systematically or comprehensively documented, it is perhaps of no surprise that the most frequently cited barrier to widespread use of commissioning is decision-makers' lack of information pertaining to costs and associated savings. Designed as a "meta-analysis," this paper compiles and synthesizes published and unpublished data from real-world commissioning and retro-commissioning projects, establishing the largest available collection of standardized information on new and existing building commissioning experience in actual buildings. We analyze results from 224 buildings, representing 30.4 million square feet of commissioned space, across 21 states. We developed a detailed and uniform methodology for characterizing the results of projects and normalizing the data to maximize inter-comparisons. For the commissioning of existing buildings, we found median energy cost savings of 15% [7% to 29% interquartile range, i.e. 25th to 75th percentiles] or 1.00/ft 2), yielding a median payback time of 4.8 years [1.2 to 16.6 years]. These results exclude non-energy impacts. When non-energy impacts are included cost-effectiveness increases considerably, and the net cost for new buildings is often zero or even negative. Cost-effective results occur across a range of building types, sizes and pre-commissioning energy intensities.
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... An expensive commissioning phase is a cause for concern. A study of 150 existing commercial buildings showed that a recommissioning could reduce the energy consummation by 15% on average [8]. Modelfree Reinforcement Learning (RL) is, as the name suggest, a model-free method to do predictive control [9,10], hence do not require the commissioning of a model. ...
... The abbreviations in the equations above are the following: R = Reward SC = Safety controller, = Zone temperature, V = Valve position, and = Hard constraint. The two sub-functions (7) and (8) are parts of the safety controller and ensure a robust behavior, incorporating a safety controller for this type of control task is supported in [39]. In [39] it is found that by incorporating a safety controller is robust behavior ensured, and a reduced convergence time is archived by reducing the action/state space to what is known to be feasible. ...
This paper presents a novel framework for Offline Reinforcement Learning (RL) with online fine tuning for Heating Ventilation and Air-conditioning (HVAC) systems. The framework presents a method to do pre-training in a black box model environment, where the black box models are built on data acquired under a traditional control policy. The paper focuses on the application of Underfloor Heating (UFH) with an air-to-water-based heat pump. However, the framework should also generalize to other HVAC control applications. Because Black box methods are used is there little to no commissioning time when applying this framework to other buildings/simulations beyond the one presented in this study. This paper explores and deploys Artificial Neural Network (ANN) based methods to design efficient controllers. Two ANN methods are tested and presented in this paper; a Multilayer Perceptron (MLP) method and a Long Short Term Memory (LSTM) based method. It is found that the LSTM-based method reduces the prediction error by 45% when compared with a MLP model. Additionally, different network architectures are tested. It is found that by creating a new model for each timestep, performance can be improved additionally 19%. By using these models in the framework presented in this paper, it is shown that a Multi-Agent RL algorithm can be deployed without ever performing worse than an industrial controller. Furthermore, it is shown that if building data from a Building Management System (BMS) is available, an RL agent can be deployed which performs close to optimally from the first day of deployment. An optimal control policy reduces the cost of heating by 19.4 % when compared to a traditional control policy in the simulation presented in this paper.
... An expensive commissioning phase gives cause for concern in the building industry. A study of 150 existing commercial buildings in the US showed that a recommissioning of the HVAC system could reduce energy consumption by 15% [16]. RL is a method for doing model-free optimal control [17], and hence does not require a model to be built. ...
This paper presents a laboratory study of Offline-trained Reinforcement Learning (RL) control of a Heating Ventilation and Air-Conditioning (HVAC) system. We conducted the experiments on a radiant floor heating system consisting of two temperature zones located in Denmark. The buildings are subjected to real-world weather. A previous paper describes the algorithm we tested, which we summarize in this paper. First, we present a benchmarking test which we conducted during spring 2021 and winter 2021/2022. This data is used in the Offline RL framework to train and deploy the RL policy, which we then tested during winter 2021/2022 and spring 2022. An analysis of the data shows that the RL policy showed predictive control-like behavior, and reduced the oscillations of the system by a minimum of 40%. Additionally, we show that the RL policy is minimum 14% more cost-effective than the traditional control policy used in the benchmarking test.
... Accordingly, cultural, political, social, economic, technological factors, and environmental acts together to influence the decision-making process of adaptive reuse of existing buildings (Bullen & Love, 2011b;Mısırlısoy & Günçe, 2016). However, the stakeholders, investors, and developers of adaptive reuse of existing buildings sometimes lack adequate planning by using a perception approach rather than an objective assessment of balancing profit-making and the continuity of the local community life and values (Bullen & Love, 2011b;Bullen, 2007;Douglas, 2006;Kurul, 2007;Mills et al., 2005). ...
The current climate change emergency has triggered a global strive for reducing carbon emissions and relevant sustainable planning for low-carbon cities. The adaptive reuse of existing buildings can be adopted to facilitate climate change mitigation progressively. This study explores the applicability of the adaptive reuse concept as a sustainable tool for climate change mitigation.
Data was gathered using the systematic literature review (SLR) approach embedded in the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol and key informant interviews with Subject Matter Experts (SMEs).
From the SLR results, 29 benefits and 16 challenges of the adaptive reuse of existing buildings were identified under four main categories each (i.e., for benefits: economic, social, cultural and historical, and environmental; and for challenges: building regulatory requirements and governance, financial, management, and complexities and uncertainties). The interview findings revealed the influence of the identified benefits and challenges, with strategies which were examined to propose a conceptual framework. The framework aims to guide relevant stakeholders on how the adaptive reuse of existing buildings could be used as a practical, sustainable measure to achieve climate change mitigation in the built environment.
... Heating, Ventilation and Air Conditioning (HVAC) systems count for 50% of the consumed energy in commercial buildings for maintaining indoor comfort [21]. Nonetheless, almost 15% of energy being utilized in buildings get wasted due to various faults (like control faults, sensor faults) which significantly occurs in HVAC systems because of lack of proper maintenance [28], [20]. In HVAC systems, an Air Handling Unit (AHU) acts as a key component. ...
We are entering a new age of AI applications where machine learning is the core technology but machine learning models are generally non-intuitive, opaque and usually complicated for people to understand. The current AI applications inability to explain is decisions and actions to end users have limited its effectiveness. The explainable AI will enable the users to understand, accordingly trust and effectively manage the decisions made by machine learning models. The heat recycler’s fault detection in Air Handling Unit (AHU) has been explained with explainable artificial intelligence since the fault detection is particularly burdensome because the reason for its failure is mostly unknown and unique. The key requirement of such systems is the early diagnosis of such faults for its economic and functional efficiency. The machine learning models, Support Vector Machine and Neural Networks have been used for the diagnosis of the fault and explainable artificial intelligence has been used to explain the models’ behaviour.
... Most buildings, especially those with complex building energy systems, have various degrees and types of operational problems. Mills et al. (2005) analyzed 85 retro-commissioning projects of existing buildings and found a total of 3,500 deficiencies, 11 per building. Correcting these deficiencies through retro-commissioning proved to be cost effective. ...
Buildings consume more than one-third of the world’s primary energy. Reducing energy use and greenhouse-gas emissions in the buildings sector through energy conservation and efficiency improvements constitutes a key strategy for achieving global energy and environmental goals. Building performance simulation has been increasingly used as a tool for designing, operating and retrofitting buildings to save energy and utility costs. However, opportunities remain for researchers, software developers, practitioners and policymakers to maximize the value of building performance simulation in the design and operation of low energy buildings and communities that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a large scale. This paper presents ten challenges that highlight some of the most important issues in building performance simulation, covering the full building life cycle and a wide range of modeling scales. The formulation and discussion of each challenge aims to provide insights into the state-of-the-art and future research opportunities for each topic, and to inspire new questions from young researchers in this field.
Implementation of sequences of operation for heating, ventilation, and air conditioning (HVAC) systems into a building automation system (BAS) is a critical process for building performance. Mistakes are commonly made while interpreting the sequences of operation from the mechanical design, converting them into BAS programs, and making ad-hoc changes to the BAS programs to address occupant complaints. This paper presents a novel inverse model-based fault detection and diagnostics (FDD) method for the discovery of BAS programming logic faults in variable air volume (VAV) air handling unit (AHU) systems. Using widely available BAS trend data types, the method first trains inverse models characterizing the heat and air mass balance of a VAV AHU system’s mixing box, heating and cooling coils, VAV terminal units, and zones. Then, the expected operational behaviour of the VAV AHU system is constructed by using ASHRAE Guideline 36 sequences of operation and the trained inverse models. Deviations from the expected operation are treated as the symptoms for the detection of programming logic faults. The method is demonstrated with BAS trend data from a 41-zone VAV AHU system in Ottawa, Canada. Two faults in supply air temperature and pressure reset programs are detected, significantly increasing fan electricity and heating energy use.
Renewable energy resources are clean and nearly everlasting in nature. These days, daylighting using renewable energy resources is of great concern. The sun is an extraordinary source of renewable energy and is an immense source of illuminance available during daytime. The available daylight can be harnessed using an appropriate daylighting device to illuminate dark spaces located at a distance from conventional fenestration. Innovative daylighting systems have the capability to deliver the desired level of illuminance in deep-plan and high-rise buildings. Tubular daylighting device (TDD), one of the passive daylighting devices, is discussed in this paper. This study reviews the effect of various parameters affecting the performance of TDD. Numerical simulation was done for 300-mm diameter transparent dome collector with two configurations of mirror light tube of a vertically projected length of 830 mm. Performance of TDD has been evaluated in the summer, equinox and the winter solstices for clear sky conditions. The results were analyzed for direct and diffuse components of sunlight separately.
Requirement of energy for human sustenance and growth has increased exponentially during the last century. The rate of rise in demand for energy has reached unprecedented levels leading to widening of gap between demand and supply of electric energy due to the scarcity of resources. The harmful effects of excessive usage of energy on the environment pose a great danger to the sustainability of our ecosystem. In this scenario, it becomes pertinent to design a strategy for increased efficiency of electricity utilization with an aim to minimize air pollution and carbon footprint. Hence, energy management systems are the need of the hour to identify the potential for improvements in energy efficiency. However, the implementation of Energy Information and Management System (EIMS) in academic institutes is extremely limited due to lack of awareness and relevant green policies. The current work presents a blueprint of Energy Information and Management System for an academic institute leading to multi-measure energy efficiency through multiple strategies including equipment operational improvements and upgrades, and occupant behavioural changes. The design of Intelligent EIMS enables energy savings relative to a baseline model, which predicts energy consumption from key parameters such as occupancy levels mapped with the timetable and operational schedule. The need for policies to be adopted by educational institutes for optimum utilization of electrical energy has been discussed and presented in the paper. In the present work, the different sub-domains/facilities of the college were primarily divided into three categories, namely facilities mapped with college timetable (like classrooms, laboratories, etc.), facilities mapped with fixed or regular schedule (like hostel mess, corridors, etc.) and facilities independent of college timetable or fixed schedule (like canteen, staffroom, common room, etc.). The two basic categories were further subdivided on the basis of scheduled usage and ad hoc usage of these facilities. Based on these categorizations, policies for energy usage were framed for these facilities, and prototype EIMS was designed and implemented at Maharaja Agrasen College, University of Delhi. 4.8% saving in the power consumption was observed post-EIMS implementation.
Retrofits to existing buildings for earthquakes, hurricanes, and other natural disasters typically focus solely on improving the structural response. However, these retrofits could provide opportunities to radically reduce the energy use of existing buildings to the point of being transformed into Zero Energy Buildings (ZEBs). Adapting strategies and technology used in new ZEBs to the retrofit of existing buildings is only possible when an event, such as a structural retrofit, offers the physical and economic opportunity to completely transform how a building operates in terms of heating, cooling, and lighting – the largest sources of energy use. This paper explores the potential of combining structural and energy retrofits into a combined multi-performance retrofit. Drawing from a database of 25 commercial buildings from the Pacific Northwest region of the U.S. that have undergone different types of retrofits, this paper focuses on the advantages and challenges of multi-performance retrofits for unreinforced masonry (URM) buildings in comparison to stand-alone structural or energy retrofits. Three multi-performance retrofits to URM buildings are described in detail to highlight the strategies used and benefits of this approach.
The Cost-Effectiveness of Commercial-Buildings Commissioning: A Meta-Analysis of Energy and Non-Energy Impacts in Existing Buildings and New Construction in the United States Creating California's Online Commissioning Case Study Database: Case Studies Go High Tech
- E Mills
- H Friedman
- T Powell
- N Bourassa
- D Claridge
- T Haasl
- M A Piette
- H Friedman
- T K Haasl
- Gillespie
Mills, E., H. Friedman, T. Powell, N. Bourassa, D. Claridge, T. Haasl, and M.A. Piette. 2004. "The Cost-Effectiveness of Commercial-Buildings Commissioning: A Meta-Analysis of Energy and Non-Energy Impacts in Existing Buildings and New Construction in the United States." Lawrence Berkeley National Laboratory Report No. 56637 http://eetd.lbl.gov/emills/PUBS/Cx-Costs-Benefits.html Friedman, H., T. Haasl. K. Gillespie. 2004. " Creating California's Online Commissioning Case Study Database: Case Studies Go High Tech. " Proceedings of the 2004 ACEEE Summer Study on Energy Efficiency in Buildings.