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Abstract In this work we present a methodology to evaluate the accuracy of methods used to quantify the uncertainty in estimated total energy savings. We focus on savings measurement and verification (M&V) approaches that use a baseline model to characterize energy use, and that forward-project the model for a counterfactual to determine avoided energy use. These approaches are common to the International Performance Measurement and Verification Protocol's (IPMVP's) Option C and Option B. This methodology can be used to evaluate the uncertainty in savings estimates that are due to model error. It has been applied to evaluate two uncertainty estimation methods, including the industry standard ASHRAE Guideline 14 approach. The evaluation used data from 69 commercial buildings and four different baseline models that span daily and hourly granularity, as well as linear and non-linear/non-parametric forms. The findings of this work indicate that the standard methods that are widely used by the M&V community for estimating the total savings uncertainty over the post-installation period tend to underestimate the uncertainty. The tendency to underestimate the uncertainty is stronger for hourly models than for daily models, due to stronger autocorrelation in model residuals at the hourly time scale. [ABSTRACT FROM AUTHOR]

New smart building technologies that offer continuous dynamic optimization of Heating, Ventilation, and Air Conditioning (HVAC) control hold promise to advance building operations for efficiency and grid response. These technologies use data from the control system to determine the analytically optimal setpoints, and then write back the optimal setpoints into the control system to minimize system energy consumption or costs. There are limited studies documenting field validations of these technologies. This paper presents the results from a long-term field evaluation of a model-predictive HVAC optimization system that installed in four commercial buildings. Energy savings analysis was conducted based on pre/post submetered energy use. Across the cohort of evaluation sites, HVAC savings following the implementation of the optimization system were mixed, ranging from 0–9%. Analysis of site operational data showed that occupant comfort was neither positively nor negatively impacted. Key technology adoption considerations and recommendations are summarized in the paper. The technology performs best when HVAC systems are in good working condition, and can be exercised to achieve the full range of its optimized setpoints–however it may not provide extensive additional savings over cases where best practice sequences of operation and reset strategies are already comprehensively implemented. [ABSTRACT FROM AUTHOR]

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model’s performance. The results show that using the gradient boosting machine model improved the R‐squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm. [ABSTRACT FROM AUTHOR]

Although buildings smaller than 4,645 m² account for nearly half of the energy used in U.S. commercial buildings, energy-efficiency programs to date have primarily focused on larger buildings. Stakeholder interviews conducted during a scoping study by Lawrence Berkeley National Laboratory (LBNL) indicated interest in energy efficiency from the small commercial building sector, provided solutions are simple and of low cost. To address this need, an energy management package (EMP)was developed to deliver energy management to small commercial buildings via HVAC contractors, because they already serve these clients and the transaction cost to market would be reduced. This energy-management approach is unique from, but often complementary to, conventional quality maintenance or retrofit-focused programs targeting the small commercial segment. This paper presents an overview of the EMP, the business model to deliver it, and preliminary demonstration findings from a pilot use of the EMP. Results from the pilot validated that contractors could deliver the EMP in 4-8 h per building per year and that energy savings of 3-5% are feasible through this approach. [ABSTRACT FROM AUTHOR]

Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of “smart” meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as “M&V 2.0”, are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs. This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V 2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. These findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption. [ABSTRACT FROM AUTHOR]

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms. • Traditional controls do not integrate distributed energy resources (DER) systems. • Deterministic policy gradient algorithm is proposed to optimize the operation of DER. • The algorithm is deployed and evaluated in a physical test building. • Tests include energy efficiency, load shift, and load shed. • An OpenAI Gym environment is made it publicly available for other researchers. [ABSTRACT FROM AUTHOR]