Electric utilities today must contain costs at a time when many need to modernize aging systems and all face major changes in technologies, customer preferences and competitive pressures.Most U.S. electric utility facilities are investor-owned, subject to rate and service regulation by state public utility commissions. Regulatory systems under which these utilities operate affect their performance and ability to meet these challenges. In this business environment, multiyear rate plans have some advantages over traditional rate regulation.The report focuses on key design issues and provides case studies of the multiyear rate plan approach, applicable to both vertically integrated and restructured states. Mark Newton Lowry and Matt Makos of Pacific Energy Group Research and Jeff Deason of Berkeley Lab authored the report; Lisa Schwartz, Berkeley Lab, was project manager and technical editor.The report is aimed primarily at state utility regulators and stakeholders in the state regulatory process. The multiyear rate approach also provides ideas on how to streamline oversight of public power utilities and rural electric cooperatives for their governing boards.Two key provisions of multiyear rate plans strengthen cost containment incentives and streamline regulation: 1. Reducing frequency of rate cases, typically to every four or five years 2. Using an attrition relief mechanism to escalate rates or revenue between rate cases to address cost pressures such as inflation and growth in number of customers, independently of the utility’s own cost Better utility performance can be achieved under well-designed multiyear rate plans while achieving lower regulatory costs. Benefits can be shared between utilities and their customers. But plans can be complex and involve significant changes in the regulatory system. Designing plans that stimulate utility performance without undue risk and share benefits fairly can be challenging.This report discusses the rationale for multiyear rate plans and their usefulness under modern business conditions. It then explains critical plan design issues and challenges and presents results from numerical research that considers the extra incentive power achieved under different plan provisions. Next, the report presents several case studies of utilities that have operated under formal multiyear rate plans or, for various reasons, have stayed out of rate cases for more than a decade. These studies consider the effect of multiyear rate plans and rate case frequency on utility cost, reliability and other performance dimensions.

The city of Dubuque, Iowa, aimed for a twofer — lower energy costs for public facilities and reduced air emissions. To achieve that goal, the city partnered with the Iowa Economic Development Authority to establish a revolving loan fund to finance energy efficiency and other energy projects at city facilities. But the city needed to understand approaches for financing energy projects to achieve both of their goals in a manner that would not be considered debt — in this case, obligations booked as a liability on the city’s balance sheet. With funding from the U.S. Department of Energy’s Climate Action Champions Initiative, Lawrence Berkeley National Laboratory (Berkeley Lab) provided technical assistance to the city to identify strategies to achieve these goals. Revolving loans use a source of money to fund initial cost-saving projects, such as energy efficiency investments, then use the repayments and interest from these loans to support subsequent projects. Berkeley Lab and the city examined two approaches to explore whether revolving loans could potentially be treated as non-debt: 1) financing arrangements containing a non-appropriation clause and 2) shared savings agreements. This fact sheet discusses both, including considerations that may factor into their treatment as debt from an accounting perspective.

In the last decade, a new policy area has emerged to boost energy efficiency in buildings that focuses on the simple action of measuring energy use as compared to buildings of similar type and size, and making that data publicly available. These efforts, referred to as benchmarking and transparency (B&T) policies, seek to unlock new energy efficiency opportunities in the country’s existing buildings by promoting data-driven decision-making and creating stronger market signals. This report focuses on the 24 state and local jurisdictions that (as of December 31, 2016) require owners of privately owned commercial buildings, multifamily buildings, or both to comply with a B&T policy. The report provides a summary of U.S. B&T policy design and implementation characteristics, reports results and impacts for jurisdictions with B&T policies, and discusses opportunities for increasing the efficacy of B&T policies, as well as suggested areas for further research. Among the findings, all but one of the B&T policy evaluation studies reviewed indicate some reduction (from 1.6% to 14%) in energy use, energy costs, or energy intensity over the two- to four-year period of the analyses. More specifically, most of the studies reviewed indicate 3% to 8% reductions in gross energy consumption or energy use intensity over a two- to four-year period of B&T policy implementation. Two additional evaluation studies indicate that there is a causal relationship between B&T policies and energy savings or energy cost savings. These documented impacts should be reviewed with some caution. While consistently showing energy savings benefits associated with B&T policies, these savings estimates should be considered preliminary because of the limited period of analyses and inconsistencies in analysis methods for the various studies. A nationally standardized method for data collection, reporting, and evaluation of B&T policies—developed with an advisory group of state and local jurisdictions, energy efficiency and evaluation experts, building owner and real estate associations, and other stakeholders—could improve the consistency and quality of B&T impact studies, providing policymakers and others with a more complete understanding of the present and future impacts of these policies.

Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However, a key barrier in the required training of CNNs is obtaining large-scale and precisely annotated imaging data. We sought to address the lack of annotated data with eye tracking technology. As a proof of principle, our hypothesis was that segmentation masks generated with the help of eye tracking (ET) would be very similar to those rendered by hand annotation (HA). Additionally, our goal was to show that a CNN trained on ET masks would be equivalent to one trained on HA masks, the latter being the current standard approach. Step 1: Screen captures of 19 publicly available radiologic images of assorted structures within various modalities were analyzed. ET and HA masks for all regions of interest (ROIs) were generated from these image datasets. Step 2: Utilizing a similar approach, ET and HA masks for 356 publicly available T1-weighted postcontrast meningioma images were generated. Three hundred six of these image + mask pairs were used to train a CNN with U-net-based architecture. The remaining 50 images were used as the independent test set. Step 1: ET and HA masks for the nonneurological images had an average Dice similarity coefficient (DSC) of 0.86 between each other. Step 2: Meningioma ET and HA masks had an average DSC of 0.85 between each other. After separate training using both approaches, the ET approach performed virtually identically to HA on the test set of 50 images. The former had an area under the curve (AUC) of 0.88, while the latter had AUC of 0.87. ET and HA predictions had trimmed mean DSCs compared to the original HA maps of 0.73 and 0.74, respectively. These trimmed DSCs between ET and HA were found to be statistically equivalent with a p value of 0.015. We have demonstrated that ET can create segmentation masks suitable for deep learning semantic segmentation. Future work will integrate ET to produce masks in a faster, more natural manner that distracts less from typical radiology clinical workflow.