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Designing a data cooperative to help make our homes more energy efficient

Our homes are responsible for about a quarter of UK energy consumption – and so making UK housing more energy efficient is a big part of the government’s net-zero carbon emissions ambition – particularly through the Green Homes Grant and the recently announced 10 point plan.

These initiatives will go some way towards helping the UK achieve its goal and should save people money by wasting less energy. But relying on physical interventions, such as adding insulation or updating your heating system, will only get us so far.

In fact, changing how we use energy within our homes has the potential to get us further and faster towards reducing, and making more efficient, our energy use. And data, too, has a part to play in our low-carbon future, through optimising how we use energy.

Something like dynamic scheduling of high-energy device operation – making better decisions about when we use our washing machine, heat our water or charge electric vehicles – can take advantage of renewable energy surpluses and links microgeneration output to local demand, and could all be enabled through sharing of our energy data.

Understanding ourselves through energy data

Like much data that’s out there in the world, the energy data that we create can often reveal a lot about us (fig.1). Energy meters that record at short intervals can detect what appliances we use and when – essentially showing when we sleep, eat or even leave the house. fig.1

So, if we want people to share data that can reveal this level of detail about their lives, in order to see how we can use it to make our homes more energy efficient, we need to be able to trust those who collect it – and have control of how it is used.

Open Data Manchester’s work with Carbon Co-op looks at whether a data cooperative would be a sufficiently trustworthy structure to enable this kind of data sharing, and if so, whether this could lead to more energy-efficient living.

Testing the coop model for using energy data

Today, most people’s homes will have some kind of ‘smart’ or connected devices in them. Often, though, the value of the data being created here isn’t realised by the user – and may be exploited by others, such as the brand that sells the devices.

The development of a ‘data cooperative’, where people or organisations agree to pool and share their data, has the potential to bring more value back to users. Looking at energy use, this could help energy consumers use data better to reduce their carbon footprint. 

Although we’re only half-way through the project, we have covered a lot of ground, mostly through workshops with a group of Carbon Co-op members and others who are interested in new forms of data organisations.

The project is split into three phases:

  • capacity and awareness building – discussing what data can and can’t do, and how data is collected and used
  • developing a ‘consent mechanism’ to establish how data could be shared
  • and finally, looking at how the cooperative will be governed

We have just finished the second phase and will be looking at governance in 2021.

During the consent mechanism work we identified existing flows of data related to energy use, as well as potential new ones, between an individual, the cooperative and third-party organisations (fig.2)

fig.2.

We went on to map the ‘points of consent’, where someone might need to say they’re happy for the data to be used in a given way, and the main functions of the data cooperative in making this happen (fig.3).

Within the data cooperative, we can see that it collects and processes data, then through a consent mechanism, releases data in an agreed way.

fig.3

The ‘processing’ function of the data cooperative would be one of ensuring consistency, making sure that the data is usable and creating a reliable supply of data, as well as functions that preserve levels of anonymity, so that things like personal details aren’t supplied to third parties.

What are we willing to share?

Coupled with mapping out technical features of the data cooperative, we explored the types of data people might be willing to share, with who and in what scenarios. What these exercises reveal is that different people value the same data differently and data has context-specific value.

Naturally, people would gladly share sensitive data in a medical emergency, but not if they were signing up for a website subscription, which indicates that there is a direct relationship between the threshold value of an individual’s data and the context in which the data is transacted.

As the session progressed, it became apparent that many of the Carbon Co-op members would freely share data if they could be confident that the sharing aligns with the cooperative’s aims, which in the case of Carbon Co-op is related to de-carbonising the energy sector, energy efficiency and the reduction of greenhouse gases.

Many cooperatives are based around specific issues and concerns with an explicit alignment of members to those issues. This raises an intriguing possibility that cooperatives, through member alignment, and enhanced governance and decision-making structures, could enable better sharing of data, given the trust brought about through aligned aims, underpinned by confidence in the way that decisions are made.

Revisiting the data flow work, we started to try to understand more about the different types of data sharing that could be enabled through the data cooperative. Flow 1 (fig.4) is the simplest of the data flows handled by the cooperative, where data is shared with the data cooperative for internal use. Data is held and processed by the cooperative, and could be used for better service provision by the cooperative.

fig.4

In Flow 2 (fig.5), we see data being shared between cooperative members, facilitated by the cooperative. This could be done through a member granting access to certain data, such as for benchmarking or understanding the performance of a particular energy-saving intervention.

fig.5

A federated flow could occur between organisations that have the same aims and data governance processes. With energy cooperatives, this could be small renewable energy generation cooperatives sharing data with energy consumer cooperatives, with the data enabling load balancing, matching supply with demand or direct energy supply.

fig.6

The third-party flow is a more traditional data-sharing or licensing agreement. This would be data shared with organisations that might not have direct alignment in aims, but could offer other types of value. These relationships could be with a broad range of organisations, from academia to private-sector businesses.

fig.7

The last identified potential flow is the open data flow that creates a common, open asset available to everyone.

fig.8

Open data released here might be seen to further the cause of the data cooperative – so in the case of the energy cooperative – it might be aggregated energy consumption, or other data that could create better awareness of energy cooperatives and low-carbon solutions.

Who’s responsible?

As the work developed, we started to look at the role of consent and the need to give people control over the choices made about how and where their data is used.

Our original thoughts were that it would essentially be a flat structure, where every member would be a data subject, and also have a certain responsibility regarding how the cooperative was run. The cooperative may have officers who carry out duties on behalf of the members, but essentially the members are in control.

But trying to understand who is ultimately responsible for data sharing – the data controller – within the data cooperative is a challenge.

Also, the need for consent within an organisation whose principle purpose is to collect, pool and share data on behalf of its members is moot. From research into the legal bases of sharing data, the data cooperative could use what is called ‘legitimate interest’ rather than ‘consent’ – as the members have all agreed to the purposes of the organisation by joining the data cooperative.

However, even though the legal basis for sharing members’ data may exist outside explicit consent and the members are aligned with the aims of the cooperative, the assumption that everyone is equally happy with the same data being shared is a dangerous one. In an earlier workshop, we found that people value the same data differently and, even though members might be aligned to the purposes of the cooperative, there may be myriad different reasons for that alignment.

Exploring the ways to get consent around data sharing

Even though the cooperative can legally share its members’ data without consent, it doesn’t mean that it should, as mentioned previously, the data cooperative’s purpose is help people regain control over their data. So, creating an environment where individual members have control over their data within the cooperative through an ethical, rather than legal consent process, could enable members to have some agency over the data being shared and this in turn could further enhance trust.

Exploring potential consent mechanisms to enable this, we converged on three – all with their own particular strengths and drawbacks.

.fig.9

With granular consent (fig.9), we give every cooperative member the option of making a decision on the type of data shared, in what form and with whom. This gives total control to the individual member, but it creates large burdens on the individual and organisation.

Similar to the ways that most of us despair when faced with endless cookie consent choices while browsing the web, the system relies on people taking time to understand why consent is needed, risking people either making poor choices or not making a choice at all.

For the cooperative, the danger is that all new data-sharing requests would have to be considered by everyone, which could affect supply and the ability of the cooperative to manage people’s data appropriately.

 

fig.10

Persona/archetype permissions (fig.10) offer a more efficient way for people to consent to the sharing of data. It reduces the burden of consent choices by members agreeing to a set of behaviours that most align with their outlook, so any additional data sharing would be done on the basis of those agreed behaviours.

This offers benefits for the data cooperative, as it would allow quicker data-sharing decisions to be attained, but care would also have to be taken regarding the design of the archetypes and how they are governed. The decisions made based on them would need to be open to scrutiny, and how they are managed and adapted over time would need to be transparent.

fig.11

The final, traffic-light consent mechanism is a broad consent mechanism that allows data to be shared on the basis of three options. The most permissive ‘green’ option allows the cooperative to share the members’ data how it sees fit. Operationally this is the most efficient, as there is no need to consult with individual members regarding how the data is used – although ultimately the data cooperative will have other specific aims and overarching governance mechanisms.

With the ‘amber’ setting, the individual can opt for granular consent for specific datasets, allowing the control of more sensitive data, such as a name or address.

The final, restricted ‘red’ option only allows the data cooperative to use the data only for its own internal purposes. This option would inhibit the cooperative from returning greater value from the data, and may beg the question as to why the member has joined the cooperative in the first place.

Although the ultimate decision around the consent mechanism hasn’t been defined yet, it is intrinsically related to how the data cooperative will be governed. This will be the focus of the second half of the data cooperatives project in 2021.

We’re very grateful for everyone who has helped with the first and second stages of this project – and look forward to sharing the findings of this work, particularly as it relates to our society’s wider goals around energy efficiency and achieving net zero.

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  1. Pingback: How to build a data cooperative with Astha Kapoor, Hays Witt and Julian Tait - Open Data Manchester

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