Sharing Energy Usage Data

Dan Sung, https://www.wareable.com/media/imager/201412/1967-original.jpg

Initial Use Case

In this connected future, buildings and homes will be better equipped to understand the usage of utilities in their homes and businesses. Citizens and owners can access this data and insights generated by third parties to make better choices around how they use these services and utilities. This information can be shared to users to benchmark against similar individuals or families to better understand their utility consumption footprint against their peers.

Data and Their Uses

Data Use

Data Needed

Data Capture Methods

Automated home and building management systems

  • Personal unit and business activity data. Occupancy data,

  • Internal environmental data (sound, temperature, humidity etc)

  • Weather and weather forecast data

  • In-home sensors (eg. Ecobee sensors, occupancy sensors) weather & weather forecast data feeds

District level optimization of energy system (storage, renewables)

  • Building utilization and consumption data

  • Historical and projected usage

  • Weather data

  • Large appliance/ HVAC systems data

  • Building sensors

  • Building Energy meters and submeters

  • Building Automation Systems

Benchmarking and performance insights to home owners

  • Personal unit and business activity data

  • Large end use appliance energy consumption data

  • Whole building energy consumption data

  • Self reported demographic data (e.g. how many people live at home etc.) to build consumer benchmarking profiles

  • Building and unit energy meters and submeters

  • In-home sensors (eg. Ecobee sensors, occupancy sensors)

  • end-use meters

  • plug-load meters

  • building energy reporting repositories

Targeted marketing for energy efficient products, conservation programs and rebates

  • Personal unit and business activity data

  • Large end use appliance energy consumption data

  • Self reported demographic data (e.g. how many people live at home etc.) to build consumer benchmarking profiles

  • Self reported appliance information (# and types of of appliances, age of appliance etc.)

  • In-home sensors (eg. Ecobee sensors, occupancy sensors)

  • end-use meters

  • plug-load meters

There are three broad categories of data that will be needed to make buildings more energy efficient, healthy and comfortable. First, utility consumption data will be collected at a per unit and building basis from devices like smart meters, home and office accessories and sensors (e.g. Nest smart thermostats). Appliance and plug-load sensors can also aid in collecting device usage for items like your refrigerator, washing machine, and electric vehicle (EV) charging stations. Second, personal demographic data will be collected to segment usage and will be captured through building records and personal reporting (e.g. surveys). Last, environmental data, such as temperature and rainfall data will be collected to provide proactive mechanisms to reduce energy consumption.

To enable these uses, the following actors will be involved in collecting, sharing and using the data: building managers and owners, public utility companies, private energy management service companies, local technology companies, citizens and business owners.

How It Works

This Civic Digital Trust will need to balance key stakeholders through this new means of sharing data. To balance the public and private interest in this use case, the civic digital trust could be managed by the public library. The public library has a long history of stewarding and sharing information for the public interest. Decision making about the trust's standards and purpose will be through a democratic process that includes citizens from the neighbourhood and the broader region. Experts, including academics from universities, will be engaged to inform the citizen decision-makers about the potential benefits and harms of data sharing. Enforcement of the decisions made by the trust happens through government oversight by the privacy commission. Data will be decentralized, as many ecosystem players will need to be present to create value for major efficiencies.

What We Heard

Changes to the Trust Model

  • Participants developed a concept of layered management of the civic digital trust. Higher levels of personal data could be managed by an increasing presence of and role for citizens. Personal and home data would be best managed with a jury of citizens supported by experts to account for the private nature of the data. In contrast, public or neighbourhood building data could primarily be managed by the library, as presented in the initial use case, with voices of citizens and government to bring additional perspectives.

  • Decision makers should include broader stakeholder groups, including not for profit entities and an ethics board to provide advocacy from their domains of expertise.

  • Government was viewed as the right institution to manage the enforcement, but participants were aware of the potential power dynamic and looked for a transparent process for enforcement and right-to-audit.

New data and data uses

  • Data uses need to have clear terms of use and purpose to ensure 'good' behaviour of the data users.

  • Participants are cognizant of the power dynamics that may arise if landlords, utility companies, and private entities are privileged with too much information without sufficient regulations and standards to limit their influence in pricing and access to basic necessities (shelter, water, electricity, etc.).

  • Participants considered opt-in features for service discounts (e.g. for energy efficiencies). They also considered no-choice opt-in features for social obligation data (e.g. sensors for security and safety of a building).

Have Your Say

Summary

Important questions were raised around whether and how users would have the ability to opt-in to provide their data. Or would that choice would be made by the device or sensor provider that has aggregated data for that particular consumer segment, facility, or community? In the latter instance, what recourse do residents have to opt-out from providing their information to the trust? Does this capability need to exist if the data provided is aggregated and/or anonymized at source?

Commercial buildings often have a more complex dynamic, where some appliances and sensors are hard-wired into the building by the owner of the building, but it is actually the tenants who utilize the building and therefore the data generated represents their use. In these instances it can often be more complex to understand whose data is being collected, analyzed and shared, especially if the use case warrants getting consent or offering opt-out capabilities to the tenant or building owner.

If the public library is to manage the civic digital trust, key questions were asked around education and skills development at the public library. These gaps and education needs will need to be more fully defined if these options are to be considered in the future.