We are pleased to announce that IB1 will be supporting The Retail Energy Code Company (RECCo) in the design and development of Ofgem’s  policy position to implement a Consumer Consent Solution (CCS). 

The CCS is a secure, digital solution that empowers energy consumers to control who can access their energy data. It enables people to easily grant, manage, review, and revoke consent. This supports transparency, consumer choice, and strong data protection across the retail energy market. 

IB1’s Role

IB1 will provide expert advisory support, assisting in the design of the Trust Framework that underpins the CCS. This framework will be central to ensuring that the service is reliable, transparent, trusted and usable by both consumers and market participants.

We will leverage our extensive experience in designing, developing, and advising on Trust Frameworks, Consent and Permission, and in cross-sector stakeholder engagement to deliver market-scale solutions. 

In particular we will bring learnings from the Perseus project, which enables SMEs to securely share emissions data with banks, calculated from their metered energy consumption, in a permission-based framework.

Sharing data with consumer or business consent is at the core of the smart data economy heralded by the Data (Use and Access) Act 2025. We are delighted to be working on a key initial focus of the Act, and intend for our learnings to be repurposed across sectors to help achieve legal, policy and technical alignment.

The report explores the opportunities and challenges of sharing data across UK National Infrastructure Systems, with a particular focus on energy, water, and transport. Icebreaker One was proud to contribute to this crucial piece of work, undertaking a landscaping exercise to assess the current state of the art in data sharing in energy, water and transport infrastructure, particularly in regard to the support of research, with a focus on gathering evidence from the private and government sectors.

Read the full report here

You can find our landscape analysis on national infrastructure data sharing with researchers, which contributed to the above report, here

Five steps that are critical to your energy data strategy

The UK has set ambitious targets to achieve net-zero carbon emissions by 2050, which require significant changes in the energy sector. A data-driven energy strategy can play a critical role in achieving these targets by identifying areas of inefficiency and optimising energy consumption patterns. Here are what we consider some of the key steps to implementing a data-driven energy strategy:

1. Data publishing and integration to enable collection: unlocking access and use
   The first step in implementing a data-driven energy strategy is to unlock data access so that it may be collected and integrated by those who need it. This includes data from sensors, meters, and other devices, as well as data from energy providers and government agencies. As such, the data value chain is complex: data will come from many different sources and/or legal entities.
   In the UK, guidelines are being developed by the Energy Data Taskforce, the Energy Systems Catapult, the Energy Network Association’s Data and Digitalisation Steering Group, and Open Energy.
   
   Unlocking access is not just a technical challenge: it requires a combination of technical, legal and operational policy processes to be in place. To address this, example(s) of specific user needs are essential: while the technical processes are often identical across use cases, the legal rules and policies will vary.
   
2. Data analysis and optimisation
   Once the data has been collected and integrated, the next step is to analyse it and identify areas of inefficiency for the use case in question. This can be done using analytical tools, ranging from basic algorithmic or statistical modelling through to predictive analytics or machine learning.
   
3. Implementation of optimisation strategies
   Once areas of inefficiency have been identified, the next step is to implement targeted optimisation strategies that are relevant and feasible for the use case in question. This could include adjusting energy consumption patterns, optimising equipment performance, or implementing new technologies like smart grids and renewable energy sources. Governments worldwide are introducing policies and regulations to support energy efficiency and adopting renewable energy.
   
4. Unlocking additional value through feedback loops
   Once targets and interventions have been identified and implemented, consider enabling access to that data as part of the process. This could include discussion with others in the data value chain (for example, those who supplied you with your input data, or those further downstream from your application) to see if there is additional value that could be unlocked. For example, supplying feedback to your data providers may open up their ability to increase their efficiency of data supply to you (both technically and in relevant insights).
   
5. Monitoring and evaluation
   Finally, it’s important to monitor and evaluate the effectiveness of the data-driven energy strategy over time. This involves tracking key performance indicators like energy consumption, carbon emissions, and cost savings, and making adjustments to the strategy as needed. Governments are establishing reporting frameworks and initiatives to help businesses and organisations monitor and report on their energy efficiency efforts.

By implementing a data-driven energy strategy, businesses and organisations can make significant progress towards achieving net-zero carbon emissions and reducing their energy costs. With the support of government policies and initiatives, and the use of advanced analytics tools, the energy sector can lead the way in adopting sustainable and efficient solutions.

How can data be leveraged to improve energy efficiency?

Energy efficiency is a critical issue facing the world today, as businesses and governments seek to reduce their carbon footprint and energy costs. One of the most promising solution areas is the ability to use data and analytics to identify areas of inefficiency and improve energy consumption patterns. Here we will explore how energy professionals can leverage data to improve energy efficiency, reduce costs and address environmental impacts.

We will start with three examples. Each requires the sharing of data between systems, sites and supply chains.

Example 1: Google’s AI-Powered Energy Management
Google has implemented an AI-powered energy management system in its data centres. These use machine-learning algorithms to optimise energy consumption patterns. Data is collected from sensors and many other sources and then uses predictive analytics to identify opportunities for energy savings. Google has reported significant results from this system, with a 40% reduction in energy consumption for cooling and a 15% reduction in overall energy usage.

Example 2: Enel’s Smart Grid Initiative
Enel, a global energy company, has implemented a smart grid initiative that leverages big data and analytics to improve energy efficiency and reduce costs. The system collects data from sensors and other sources, and uses machine-learning algorithms to optimise energy distribution patterns in real-time. As a result, Enel has reported a 20% reduction in energy losses and a 30% reduction in maintenance costs.

Example 3: Smart Lighting in Amsterdam
The city of Amsterdam has implemented an intelligent lighting system that uses data analytics to optimise energy usage. The system collects data from sensors and cameras and uses machine-learning algorithms to adjust lighting levels based on the presence of pedestrians and cyclists. As a result, the city has reported a 30% reduction in energy consumption for street lighting and a 50% reduction in maintenance costs.

We can see from these examples that leveraging data and analytics is a powerful way for energy professionals to improve energy efficiency and reduce costs. By collecting and analysing data, and implementing targeted optimisation strategies, businesses and governments can achieve their energy goals and positively impact the environment.

Archive video:

Who:

• Kathryn Corrick, Strategy & Development Director, Icebreaker One (chair)
• Gavin Starks, CEO & Founder, Icebreaker One 
• More speakers TBC

What:

Join us to celebrate innovative visualisations in UK energy data!

Visualisations are a great way for people to understand, feel inspired and engage with energy data, in turn helping accelerate the transition to Net Zero.

At this Showcase you will be introduced to exciting energy data visualisations in the UK right now. You will hear from our panel of expert judges and the winners of the Showcase will be announced!

The Showcase is part of the Energy Data Visibility Project led by a partnership between Icebreaker One, Arup, and Hippo Digital, and commissioned by the Department of Business, Energy and Industrial Strategy. Developing an innovative service that will connect the UK’s current patchwork of energy data sources to improve their visibility and accuracy.

Programme: The Energy Data Visibility Project (EDVP) forms part of the Modernising Energy Data (MED) programme being commissioned by BEIS, Ofgem and Innovate UK.

This project update is informal and not yet formally endorsed by BEIS. Formal notices will be marked as such.

Summary

We are seeking feedback on a system of data classification proposed for the Open Energy ecosystem. Comments are welcomed from all energy sectors organisations and users of energy data. Feedback will be used to shape the Open Energy data classes policy and inform subsequent development of data access and licensing policies (to be consulted on in due course). The consultation is open until 30 April 2021 and can be accessed here.

Background

Open Energy aims to modernise access to energy and related data and break down barriers to data sharing. Open Energy will make it easier to both share and access data, supporting the sector’s drive towards decarbonisation, as well as related social and economic benefits. The project aims to serve all energy sector actors looking to share data, access data, or both.

Open Energy builds on learning from Open Banking – identifying which elements are transferable to the energy sector, and which require adaptation or fresh thinking. Our ethos emphasises openness, transparency and sector engagement to ensure that the project meets the widest possible variety of needs. We are now seeking feedback on the first of a set of three policies aiming to navigate one of the most significant differences between Open Energy and Open Banking: the diversity of datasets shared within their respective ecosystems. 

Open Banking only handles two categories of data: open data and personal data. By contrast, Open Energy will incorporate more categories of data that have varied levels of sensitivity. In order to handle this complexity, and to ensure data is appropriately protected, Open Energy is developing a system of data classification. This consultation focuses on sharpening the descriptions, examples and criteria forming the data classes proposed. Follow-up consultations exploring data access and data licensing will take place later. 

Proposal

We propose a system of six data classes, graded across three dimensions of sensitivity: personal, commercial, and security. Proposed classes are presented in Table 1 of the consultation document. It is proposed that – once finalised – a definition, specification and dataset examples for each class will be provided in the Open Energy Operational Guidelines. Data Providers (organisations sharing data via the Open Energy ecosystem) will then assess their datasets and self-allocate them to a class, prior to sharing them via Open Energy. 

Open Energy data classes are designed to supplement, not replace, the Modernising Energy Data Best Practice Guidance (current version Point 12) determining whether data should be made Open, Shared or Closed. In particular, Open Energy data classes are designed to provide nuance to different classes of Shared data, with different sensitivity profiles.

How can you help?

We are seeking feedback on the proposal through our consultation here. It takes around 30 minutes to respond. The consultation explores questions including: 

• Are proposed data classes appropriate and clear?
• Are example datasets given for each class are accurate?
• Are any types of sensitivity missing from our analysis?

The consultation is open until 30 April 2021 and responses are encouraged from all actors in the energy sector, or who work with energy and related data. Any queries should please be directed to emily.judson@ib1.org. 

Introduced by the EU in 2007, EPCs provide information on a property’s energy usage, using a ranking system of A – (the most energy efficient) to G – (the least energy efficient) as well as suggesting methods to improve energy efficiency. To paint a picture of the task at hand, 71 percent of UK homes do not meet EPC standard C, the minimum requirement to bring buildings in line with net zero.

Open data and accuracy

While EPC data could prove to be a useful instrument in framing policy ideas centred around a green economic recovery, we should first assess the potential flaws in this data. According to a report by Spec, around 2.5 million EPCs could be wrong because of errors in measurement standards and practices. What’s more, the Property Technology solution states that 90 percent of EPCs lodged use simple averages or standardised values rather than actual measurements.

Another seemingly glaring error is the depth of inspection. If an assessor is unable to see the use of energy efficient measures, such as loft insulation, then the software used assumes an insulation level in line with when the property was built. Meaning, that for a house built over 100 years ago, zero insulation will be assumed, providing an inaccurate portrayal of housing efficiency.

EPC data is publicly available in England and Wales under a UK Open Government license v3.0 with the exception of address data which is provided under a Royal Mail Copyright Notice. Open data access to real-time EPC datasets can improve functionality and enable interoperability between other government owned databases as well as providing value to academia and businesses. The UK government’s ‘Action Plan’ released in September 2020, lays out plans for the creation of a new EPC register by the end of 2020, addressing issues of accuracy and openness while also moving towards EPC ratings that are more reflective of actual household energy use, an important move given the increased number of people working remotely.

A tool for framing policy

Across Europe, EPC data is being utilised for policy making and monitoring. Bulgaria, for example, set its national recovery strategy using EPC data. And, in a pilot scheme run by the Energy Efficient Mortgages Initiative, lenders will be offering lower interest rates on mortgages for both new build homes and renovations based on EPCs. KfW’s Environmental and Energy Efficiency Programme provides subsidies to building owners, including long-term loans with low interest rates and grants. The German development bank uses EPCs to determine the efficiency level of a household, rewarding higher efficiency levels with higher funding.

EPC data can be a critical metric in forming policy and shaping green initiatives. And, with domestic homes alone accounting for around 15 percent of UK’s greenhouse gas emissions, the data provides both public and private owners and tenants with vital information, legal certainty and incentives to improve their household energy efficiency. But, in order for the government to meet its commitment to net-zero by 2050, EPC data needs to be more accurate, reliable and open. 

Find out more

On Monday 16 November 2020, our team will be hosting a session to discuss how policy interventions can help local governments, cities and regions accelerate innovation and drive toward a net-zero economic recovery from Covid-19 at London Climate Action Week 2020. Their remarks will draw on the preliminary findings of our net-zero Covid-19 recovery programme, Project Cygnus. To find out more about the findings which will touch on some of the content elaborated above, sign up for free here.

With a view to better explaining the potential for open and shared data to address climate change, the Open Data Institute (ODI) and Icebreaker One have developed a Data Spectrum for Energy.

Icebreaker One is exploring how to rapidly evolve financial and energy systems to embed net-zero into all investments in a manner that is demonstrable, provable and uses data to hold organisations to account.

This work is built upon in our development of Shared Data.

If you have feedback on our joint Data Spectrum for Energy, please get in touch.