Background

1) Meters and Smart Meters - where does energy data come from?

Energy data is generated by hardware devices that measure electricity and natural gas flow. A device like this is generally referred to as a “meter” (though this is distinct from the software-based “EEmeter” - see Methods Overview). The most common and ubiquitous measuring device is a utility-owned meter used for determining billing. Some utilities have upgraded their meters to provide hourly or 15-minute interval measurements. These so-called “smart meters” use Advanced Metering Infrastructure (AMI) to transmit data back to utilities for processing in near-real time. Other devices that generate energy data include sub-meters, external sensors, and embedded sensors.

Note

The “smart” in smart meter can be a bit of a misnomer. Despite higher measurement frequency and wireless data transmission, these smart meters collect essentially the same data that electricity meters did in the 1950s. Each meter datapoint consists of a timestamp and an incremental value of consumption. We call this string of data characterized by paired sets of timestamps and meter readings a trace. Traces form the basis of the energy modeling in the EEmeter.

Just like the odometer in your car doesn’t tell you how fast you are traveling, the meter on your house doesn’t tell you how much energy you have consumed. Consumption must be calculated. In the past, energy companies simply determined your rate of consumption by taking monthly meter readings and calculating the difference. With smart meters, these datapoints can be captured more frequently and with greater precision, allowing for more sophisticated forms of billing.

2) Measuring Energy Savings and the Transition to Demand Side Management

The OpenEEmeter replaces traditional approaches to program-related energy measurement. Utilizing newly available smart meter data, the OpenEEmeter solves the problem of measuring energy savings and opens new doors for managing demand side programs.

Historically, energy savings have been measured in one of three ways. The first (and least costly) approach is to take laboratory measurements of different energy-consuming devices (e.g., light bulbs) and calculate the difference in consumption from one to the next, then estimate the savings over a given period of time, taking into consideration typical usage patterns. This first approach is limited by the accuracy and availability of physical models.

The second (and most costly) approach samples consumption data prior to and following an intervention of some sort (e.g., an energy efficiency retrofit), and estimates savings after controlling for building-specific factors like occupancy, temperature, energy intensity, etc. This second approach is limited by low availability of data describing these building-specific factors (thus making it very costly).

A third (post-hoc) approach has recently emerged that takes a population-level sample of similar buildings and compares with a treatment group of buildings that have received an energy efficiency upgrade (or other intervention). This approach assumes that all buildings will be affected equally by exogenous factors, leaving only endogenous factors (i.e., the efficiency upgrade) to account for the energy consumption difference.

In the analog era of traditional meters and monthly bills, efforts to improve energy efficiency emphasized fairly static and permanent changes in consumption. A whole-home retrofit, for example, would reduce energy demand without requiring any additional behavioral or lifestyle changes. A one-time intervention would provide years of benefit, and our metering technology at the time provided a way to measure the performance of these measures.

With the introduction of smart meters, utilities have transitioned from simple efficiency programs to a suite of programs under the umbrella of demand side management (DSM). These new measures fall into three broad categories including time of day, demand, and net metering. The OpenEEmeter expands the programmatic interface of energy efficiency to engage with emergent technologies and market based demand side engagement programs.

3) How the OpenEEmeter is valuable: Baselining, Normalization, and Modeling Energy Use

Smart meter data allows for more complexity in statistical models. Rather than relying on simple regression experiments to normalize energy consumption, analysts can parse the impact of exogenous and endogenous factors independently and iteratively. The notion of baseload energy use can even be disaggregated into multiple demand states. For example, a home will use very little energy when empty, a bit more when occupied, and a large amount when appliances and heating or cooling systems are operating. These demand states can be measured against various sorts of interventions, thus enabling both traditional energy efficiency savings measurements, but also leveraging modern load balancing tools.

The OpenEEmeter calculates energy savings in real time by selecting a sample of consumption data prior to an intervention, weather-normalizing it to establish a baseline, and calculating the difference between projected energy usage and actual energy usage following the intervention. This method maintains the cost-effectiveness of the naive predicted savings approach, the real-world integrity of the building efficiency approach, without sacrificing on time as with the post hoc control group approach.