(PNNL) to identify monitoring and control needs for small-and medium-sized commercial buildings, and to recommend possible solutions. The scope of this study is to characterize the monitoring and controls needs for the various end uses (for both efficiency and demand response), determine requirements to develop control packages, and calculate the target cost of doing so. Section 1.0 introduces the study scope and analysis approaches used. Discussions regarding the number of buildings in the U.S that comprise "small-size" and "medium-size" buildings, their lack of building automation systems (BAS) and potential energy improvements, as well as challenges, are detailed in this section. Section 2.0 covers the characterization of both small-and medium-sized buildings. Drawing upon Energy Information Administration's Commercial Building Energy Consumption Survey data from various surveys, detailed discussions of energy end-use and electrical end-use consumption values are provided. This section spring boards into further discussions for the various end-use loads and the present penetration of "intelligent" controls in the existing market. Discussions of existing and possible future control methods, strategies and concepts that are applicable (including heating, ventilation and air conditioning (HVAC); lighting and miscellaneous end-use loads) complete this section. Section 3.0 discusses the different communication architectures that might be found in a small-or medium-sized building BAS, as it relates to the communication networks needed to support them. This discussion covers the different technologies that have been in place (older) or are becoming more prevalent (newer), and how they work. This includes wired solutions, wireless solutions or a combination of both (hybrid wired-wireless) networks and industry standards, open and proprietary protocols. For each solution, the limitations of each technology are detailed (speed, bandwidth, reliability, etc.). Cost factors are also discussed because this relates to how these systems are being pushed to the market, and their acceptance (or lack of). Section 4.0 describes the BAS, as has historically been seen and known in large building applications and the small-or medium-sized building applications. This section describes the history of BASs and how they have evolved and improved over time, and summarizes their core functions. This description proceeds to discuss the major architectural requirements needed by new BASs to allow for greater penetration in the existing building stock in the U.S. This section concludes by providing three different options of what a future BAS configuration might look like for either a small-sized building (two different options) or for a medium-sized building (one option). Section 5.0 presents the requirements and capabilities of various devices used to monitor and control different end-use loads found in small-and medium-sized buildings. This includes a robust presentation of the different requirements for the gateway, master controller, co...

This paper presents an estimate of the potential for energy efficiency improvements in the U.S. building sector by 2030. The analysis uses the Energy Information Administration's AEO 2007 Reference Case as a business-as-usual (BAU) scenario, and applies percentage savings estimates by end use drawn from several prior efficiency potential studies. These prior studies include the U.S. Department of Energy's Scenarios for a Clean Energy Future (CEF) study and a recent study of natural gas savings potential in New York state. For a few end uses for which savings estimates are not readily available, the LBNL study team compiled technical data to estimate savings percentages and costs of conserved energy. The analysis shows that for electricity use in buildings, approximately one-third of the BAU consumption can be saved at a cost of conserved energy of 2.7 ¢/kWh (all values in 2007 dollars), while for natural gas approximately the same percentage savings is possible at a cost of between 2.5 and 6.9 $/million Btu (2.4 to 6.6 $/GJ). This cost-effective level of savings results in national annual energy bill savings in 2030 of nearly $170 billion. To achieve these savings, the cumulative capital investment needed between 2010 and 2030 is about $440 billion, which translates to a 2-1/2 year simple payback period, or savings over the life of the measures that are nearly 3.5 times larger than the investment required (i.e., a benefit-cost ratio of 3.5).ii

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In support of DOE's sensors and controls research, the goal of this project is to move toward integrated building to grid systems by building on previous work to develop and demonstrate a set of load characterization measurement and evaluation tools that are envisioned to be part of a suite of applications for transactive efficient buildings, built upon data-driven load characterization and prediction models. This will include the ability to include occupancy data in the models, plus data collection and archival methods to include different types of occupancy data with existing networks and a taxonomy for naming these data within a Volttron 1 agent platform. This research was conducted to: determine desired characteristics of, and technical feasibility of, new sensors that can inexpensively monitor the number of building occupants; explore how existing systems in buildings can be used to estimate the number of occupants as a function of time; and  use energy savings Measurement and Verification (M&V) methods to quantify changes in building energy performance, both with and without the use of occupancy data. Virtual SensingWe have identified more than a dozen potential data sources for virtual occupancy sensing in buildings, and collected sample data on eight of them from LBNL buildings. Specifically, we acquired data from LBNL's telephone system, its Wi-Fi infrastructure, and several sources from the IP network infrastructure. Each source has its own advantages, disadvantages, and peculiarities. A general feature of most sources is that data could be extracted as frequently as desired, and it is almost as easy to analyze results for many buildings as it is to do so for a single one. Since all hardware required is already present in buildings, the implementation cost is close to zero. The technology appears to be highly replicable and scalable. In the primary study buildings, occupancy patterns are readily visible in the data, particularly arrival, departure, and lunchtime. Weekends and holidays are also similarly quite obvious in the data. Measurement and Verification (M&V) AgentCurrent Practice: No occupancy data We used the M&V Agent that was developed for the Transactional Network project that contains a baseline model to a) predict load based on historic building load and weather data, and b) use the load predictions to quantify changes in energy use over time. Therefore, the Agent can be DisclaimerThis document was prepared as an account of work sponsored by the United States Government.

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