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Buildings account for about 40 percent of the total energy consumption in the U.S. and emit approximately one third of greenhouse gas emissions. But they also offer tremendous potential for achieving significant greenhouse gas reductions with the right savings strategies. With an increasing amount of data from buildings and advanced computational and analytical abilities, buildings can be made “smart” to optimize energy consumption and occupant comfort. Smart buildings are often characterized as having a high degree of data and system integration, connectivity and control, as well as the advanced use of data analytics. These “smarts” can enable up to 10–20% savings in a building, and help ensure that they persist over time. In 2009, Microsoft Corporation launched the Energy-Smart Buildings (ESB) program with a vision to improve building operations services, security and accessibility in services, and new tenant applications and services that improve productivity and optimize energy use. The ESB program focused on fault diagnostics, advanced analytics and new organizational processes and practices to support their operational integration. In addition to the ESB program, Microsoft undertook capital improvement projects that made effective use of a utility incentive program and lab consolidations over the same duration. The ESB program began with a pilot at Microsoft's Puget Sound campus that identified significant savings of up to 6–10% in the 13 pilot buildings. The success of the pilot led to a global deployment of the program. Between 2009 and 2015, there was a 23.7% reduction in annual electricity consumption (kWh) at the Puget Sound campus with 18.5% of that resulting from the ESB and lab consolidations. This article provides the results of research conducted to assess the best-practice strategies that Microsoft implemented to achieve these savings, including the fault diagnostic routines that are the foundation of the ESB program and organizational change management practices. It also presents the process that was adopted to scale the ESB program globally. We conclude with recommendations for how these successes can be generalized and replicated by other corporate enterprises. [ABSTRACT FROM PUBLISHER]

Building energy information systems (EIS) are a powerful customer-facing monitoring and analytical technology that can enable up to 20 % site energy savings for buildings. Few technologies are as heavily marketed, but in spite of their potential, EIS remain an underadopted emerging technology. One reason is the lack of information on purchase costs and associated energy savings. While insightful, the growing body of individual case studies has not provided industry the information needed to establish the business case for investment. Vastly different energy and economic metrics prevent generalizable conclusions. This paper addresses three common questions concerning EIS use: what are the costs, what have users saved, and which best practices drive deeper savings? We present a large-scale assessment of the value proposition for EIS use based on data from over two-dozen organizations. Participants achieved year-over-year median site and portfolio savings of 17 and 8 %, respectively; they reported that this performance would not have been possible without the EIS. The median 5-year cost of EIS software ownership (up-front and ongoing costs) was calculated to be $1800 per monitoring point (kilowatt meter points were most common), with a median portfolio-wide implementation size of approximately 200 points. In this paper, we present an analysis of the relationship between key implementation factors and achieved energy reductions. Extent of efficiency projects, building energy performance prior to EIS installation, depth of metering, and duration of EIS were strongly correlated with greater savings. We also identify the best practices use of EIS associated with greater energy savings. [ABSTRACT FROM AUTHOR]

We present a methodology to evaluate the accuracy of baseline energy predictions. To evaluate the predictions from a computer program, the program is provided with electric load data, and additional data such as outdoor air temperature, from a “training period” of at least several months duration, and used to predict the energy use as a function of time during the subsequent “prediction period.” The predicted energy use is compared to the actual energy use, and errors are summarized with several metrics, including bias and mean absolute percent error (MAPE). An important feature of this methodology is that it can be used to assess the predictive accuracy of a model even if the model itself is not provided to the evaluator, so that proprietary tools can be evaluated while protecting the developer’s intellectual property. The methodology was applied to evaluate several standard statistical models using data from four hundred randomly selected commercial buildings in a large utility territory in Northern California; the result is a statistical distribution of errors for each of the models. We also demonstrate how the methodology can be used to assess the uncertainty in baseline energy predictions for a portfolio of buildings, which is an issue that is important for the design of utility programs that incentivize energy savings. The findings of this work can be used to (1) inform technology assessments for technologies that deliver operational and/or behavioral savings; and (2) determine the expected accuracy of statistical models used for automated measurement and verification (M&V) of energy savings. [ABSTRACT FROM AUTHOR]

Abstract: This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to M&V (measurement and verification) of whole-building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a “training period” and using the model to predict total electricity consumption during a subsequent “prediction period.” We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best-performing model as judged by one metric was not the best performer when judged by another metric. [Copyright &y& Elsevier]

Editors' notes: [ABSTRACT FROM AUTHOR]

Abstract The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, 'non-routine events' (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model's independent variables. In the M&V process NREs must be accounted for as 'adjustments' to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. In this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics. [ABSTRACT FROM AUTHOR]