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We analyze data from 36 commercial and government buildings that participated in a Demand Response (DR) program in California, to investigate the extent to which DR load shed in each building depends on outdoor air temperature, and whether the load shed varies systematically from year to year. Our baseline model has substantially lower error than other standard models but uncertainty in the load shed is still an impediment to addressing these questions. The model is accurate enough in 29 buildings to be used to investigate the relationship between outdoor temperature and the DR load shed, and data availability and accuracy are sufficient to investigate year-to-year persistence of load shed in 19 buildings. We find that for buildings in this dataset, most buildings shed several percent of their load during DR events. In about two thirds of buildings, higher outdoor air temperature lead to slightly reduced load shed. Year-to-year changes in load shed were generally small, except that in several buildings the load shed was small or nonexistent in the first year of participation in the program and increased subsequently.

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. 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 of energy savings.

This report documents a demonstration that compared the energy consumption and efficiency of a limited sample size of server-type IT equipment from different manufacturers by measuring power at the server power supply power cords. The results are specific to the equipment and methods used. However, it is hoped that those responsible for IT equipment selection can used the methods described to choose models that optimize energy use efficiency.The demonstration was conducted in a data center at Lawrence Berkeley National Laboratory in Berkeley, California. It was performed with five servers of similar mechanical and electronic specifications; three from Intel and one each from Dell and Supermicro.Server IT equipment is constructed using commodity components, server manufacturer-designed assemblies, and control systems. Server compute efficiency is constrained by the commodity component specifications and integration requirements. The design freedom, outside of the commodity component constraints, provides room for the manufacturer to offer a product with competitive efficiency that meets market needs at a compelling price. A goal of the demonstration was to compare and quantify the server efficiency for three different brands. The efficiency is defined as the average compute rate (computations per unit of time) divided by the average energy consumption rate. The research team used an industry standard benchmark software package to provide a repeatable software load to obtain the compute rate and provide a variety of power consumption levels. Energy use when the servers were in an idle state (not providing computing work) were also measured.At high server compute loads, all brands, using the same key components (processors and memory), had similar results; therefore, from these results, it could not be concluded that one brand is more efficient than the other brands. The test results show that the power consumption variability caused by the key components as a group is similar to all other components as a group. However, some differences were observed. The Supermicro server used 27 percent more power at idle compared to the other brands. The Intel server had a power supply control feature called cold redundancy, and the data suggest that cold redundancy can provide energy savings at low power levels. Test and evaluation methods that might be used by others having limited resources for IT equipment evaluation are explained in the report.

With advances in energy metering, communication, and analytic software technologies,providers of Energy Management and Information Systems (EMIS) are opening new frontiers inbuilding energy efficiency. Through their engagement platforms and interfaces, EMIS productscan enable energy savings through multiple strategies including equipment operationalimprovements and upgrades, and occupant behavioral changes. These products often quantifywhole-building savings relative to a baseline period using methods that predict energyconsumption from key parameters such as ambient weather conditions and operation schedule.These automated baseline models streamline the M&V process and are of critical importance toowners and utility program stakeholders implementing multi-measure energy efficiencyprograms.This paper presents the results of a PG&E Emerging Technology program, undertaken toadvance capabilities in evaluating EMIS products for building-level baseline energy modeling. Ageneral methodology to evaluate baseline model performance was developed and used withhourly whole-building energy data from nearly 400 small and large commercial buildings.Evaluation metrics describing model accuracy were identified and assessed for theirappropriateness in describing model baseline performance, as well as their usefulness foridentifying and pre-screening buildings for whole-building savings estimation suitability. Thestate of five public-domain models was assessed using the methodology and test data set, andimplications for whole building M&V described. Finally a protocol was developed to test EMISvendor's proprietary models while navigating practical issues concerning test data security,vendor intellectual property, and maintaining appropriate testing blinds, while processing alarge data set. Ongoing work entails stakeholder vetting, demonstration of the test procedureswith new baseline models solicited from the public, and publication of the results for industryadoption.

Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the baseline energy use). Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions. However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.

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  Measurement  and  Verification  (M&V)  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.

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 Measurement and Verification (M&V) 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.

The goal of this project was to answer the following questions concerning response to a future anthrax release (or suspected release) in a building: 1. Based on past experience, what rules of thumb can be determined concerning: (a) the amount of sampling that may be needed to determine the extent of contamination within a given building; (b) what portions of a building should be sampled; (c) the cost per square foot to decontaminate a given type of building using a given method; (d) the time required to prepare for, and perform, decontamination; (e) the effectiveness of a given decontamination method in a given type of building? 2. Based on past experience, what resources will be spent on evaluating the extent of contamination, performing decontamination, and assessing the effectiveness of the decontamination in a building of a given type and size? 3. What are the trade-offs between cost, time, and effectiveness for the various sampling plans, sampling methods, and decontamination methods that have been used in the past?