Annika Todd

Energy Savings Lifetimes and Persistence

This technical brief explains the concepts of energy savings lifetimes and savings persistence and discusses how program administrators use these factors to calculate savings for efficiency measures, programs and portfolios. Savings lifetime is the length of time that one or more energy efficiency measures or activities save energy, and savings persistence is the change in savings throughout the functional life of a given efficiency measure or activity. Savings lifetimes are essential for assessing the lifecycle benefits and cost effectiveness of efficiency activities and for forecasting loads in resource planning. The brief also provides estimates of savings lifetimes derived from a national collection of costs and savings for electric efficiency programs and portfolios.

Predicting Baseline for Analysis of Electricity Pricing

To understand the impact of new pricing structure on residential electricity demands, we need a baseline model that captures every factor other than the new price. The standard baseline is a randomized control group, however, a good control group is hard to design. This motivates us to devlop data-driven approaches. We explored many techniques and designed a strategy, named LTAP, that could predict the hourly usage years ahead. The key challenge in this process is that the daily cycle of electricity demand peaks a few hours after the temperature reaching its peak. Existing methods rely on the lagged variables of recent past usages to enforce this daily cycle. These methods have trouble making predictions years ahead. LTAP avoids this trouble by assuming the daily usage profile is determined by temperature and other factors. In a comparison against a well-designed control group, LTAP is found to produce accurate predictions.

Insights from Smart Meters. Ramp-up, dependability, and short-term persistence of savings from Home Energy Reports

Smart meters, smart thermostats, and other new technologies provide previously unavailable high-frequency and location-specific energy usage data. Many utilities are now able to capture real-time, customer-specific hourly interval usage data for a large proportion of their residential and small commercial customers. These vast, constantly growing streams of rich data (or big data) have the potential to provide novel insights into key policy questions about how people make energy decisions.

Insights from Smart Meters: Identifying Specific Actions, Behaviors, and Characteristics That Drive Savings in Behavior-Based Programs

In this report, we use smart meter data to analyze specific actions, behaviors, and characteristics that drive energy savings in a behavior-based (BB) program. Specifically, we examine a Home Energy Report (HER) program. These programs typically obtain 1% to 3% annual savings, and recent studies have shown hourly savings of between 0.5% and 3%. But what is driving these savings? What types of households tend to be “high-savers”, and what behaviors are they adopting? There are several possibilities: one-time behaviors (e.g., changing thermostat settings); reoccurring habitual behaviors (e.g., turning off lights); and equipment purchase behaviors (e.g., energy efficient appliances), and these may vary across households, regions, and over time.