Cambridge Energy Data Lab

Cambridge Energy Data Lab: Smart Energy Analysis Suite

Cambridge, United KingdomJapan
Year Founded:
Organization type: 
for profit
Project Stage:
$1 million - $5 million
Project Summary
Elevator Pitch

Concise Summary: Help us pitch this solution! Provide an explanation within 3-4 short sentences.

The Smart Energy Analysis Suite (SEAS) is a next-generation analytical software package that utilises the growing wealth of energy data to allow utilities to reduce their costs, operate more sustainably, and interact more transparently with their customers.

WHAT IF - Inspiration: Write one sentence that describes a way that your project dares to ask, "WHAT IF?"

What if the growing resources of energy consumption data could be used to make the energy sector more efficient and sustainable?
About Project

Problem: What problem is this project trying to address?

The UK domestic energy sector is in a state of rapid change. Energy data availability is growing vastly with the impending rollout of smart meters, but uncertainty surrounds the benefits that can be gained. In many aspects, the UK is a model for evolving energy sectors around the world; understanding what works here is the key to sharing the lessons with the rest of the world, allowing energy economies to operate cheaper and more sustainably.

Solution: What is the proposed solution? Please be specific!

The Smart Energy Analysis Suite (SEAS) is a next-generation analytical software package designed to derive value from energy data and pass the benefits to all parties in the consumption chain. SEAS utilises cutting-edge machine learning and big data technologies to provide an adaptable, scalable solution that allows utilities to reduce their operating costs and interact more transparently with their customers. It does this through solid data management, aggregation, and cleaning; the forecasting of future electricity usage on a wide range of analytical levels; insightful visualisation for the utility and household alike; and the derivation of behavioural patterns to pinpoint customers eligible for savings.
Impact: How does it Work

Example: Walk us through a specific example(s) of how this solution makes a difference; include its primary activities.

SEAS can manage data from multiple sources, such as smart meters, smart devices, and surveys, and also can detect missing or faulty data. SEAS can accurately predict electricity consumption of individual users in both short and long-term, helping utilities reduce generation waste and operating costs. Additionally, the forecasted profiles can be paired with rates to more accurately predict monthly bills and gain consumer trust. SEAS can simulate the effects of offering customised tariff plans, especially using time based rates. The ultimate goal of this is to identify users to incentivise to shift load off peak morning and evening times—where rates jump and extra generation is required—thereby reducing costs and environmental impact.

Impact: What is the impact of the work to date? Also describe the projected future impact for the coming years.

While SEAS will launch officially in 2016, the research behind it has been developed and demonstrated by Cambridge Energy Data Lab over the past two years. For example, the forecasting algorithms are currently used by our Japanese partner Enechange. Enechange attracts customers who are looking to save on their energy bills and, after collecting information about the customer’s household (number of members, appliance types, etc.), predicts the monthly bill for all available energy plans. The accuracy of our forecasting method has been a critical factor for building consumer confidence in Enechange, making it the top energy plan switching website in Japan with 500,000 monthly users. Beyond 2016, we hope to offer SEAS to the EU and any other countries looking to improve their energy sector through demand-side management, demonstrating the value of smart meter implementation in the process.

Spread Strategies: Moving forward, what are the main strategies for scaling impact?

Over the next year, we are targeting UK utilities and utility service providers. This timing coincides with the “go live” date in April for the UK smart meter rollout, when utilities will begin receiving live data en masse. We hope to be implemented in three customer engagements by the end of the year, bringing in monthly revenues of over £30,000 and gaining a fuller picture of the benefits as they transform the customer’s operations. We hope to use these findings to fuel our research, present on the academic and industrial circuits and offer SEAS to customers in Europe and around the world.

Financial Sustainability Plan: What is this solution’s plan to ensure financial sustainability?

Cambridge Energy Data Lab currently relies on consulting engagements with energy sector companies and government institutions. We were profitable in the first year after founding and plan to continue consulting as a means to grow our brand and fund our research. SEAS represents the first step towards a product-centric model, since consulting services are more difficult to scale.

Marketplace: Who else is addressing the problem outlined here? How does the proposed project differ from these approaches?

While energy data analysis is a critical point of examination in the sector, we find many paths to differentiate ourselves. For example, there are many companies, both large and small, addressing energy visualisation methods and analytical abilities. Very few of these companies, however, rely on advanced statistical methods or machine learning. We highlight our academic background and PhD-level analysis capabilities to tackle the hardest problems in this arena. In many aspects, these “competing” companies could become customers, licensing our algorithms to enhance their capabilities.

Founding Story

Cambridge Energy Data Lab was founded by several Japanese investors as an exploratory research team developing novel data analysis methods. These investors saw the UK as a testbed for evolving energy sector trends—namely energy market liberalisation and wide scale smart meter implementation. Targeting Cambridge as an ideal headquarters because of the university and entrepreneurial community, a team of highly educated data scientists and researchers formed to answer the singular question: how can the growing resources of energy consumption data be used to make the energy sector more efficient and sustainable? After two years of intensive research, SEAS is the answer to that question.


Cambridge Energy Data Lab is comprised of a team highly educated data scientists, researchers and entrepreneurs: Yoshiyuki Iwasaki, CEO (based in Hong Kong): Founder of EnergyPlanCompany, current majority owner of CEDL; founder and director of Epco, Inc., a current business partner of CEDL; Dimitry Foures, Founding Member and Data Scientist: PhD in Fluid Dynamics from University of Cambridge; winner of the Smith-Knight/Rayleigh-Knight prize; content creator for Cambridge Coding Academy; author of four academic papers and planning to publish a fifth on the TOU algorithm in December 2015; Giuseppe Vettigli, Data Scientist: MSc Computer Science from University of Naples Parthenope; seven publications related to logic programming, AI, visualisation, and machine learning; numerous projects and code packets created for scientific computing and data visualisation in Python; content creator for Cambridge Coding Academy; Paul Monroe, Business Manager: MPhil Technology Policy from University of Cambridge; previous experience in engineering—including working for NASA—teaching, consulting and academic research; Sibel Yuzudik, Energy Markets and Policy Analyst: PhD candidate at Imperial College London; over two years of intense research experience in energy markets and environmental analysis; Masayuki Tanaka, Developer (based in Japan): MS Molecular Biology from University of Tokyo; engineer with over 7 years of experience
Please confirm how you heard about the Unilever Awards:

ideaSpace Cambridge bulletin board posting

Please confirm your role in the initiative (eg Founder/co-Founder) and your organisational title:

Applying on behalf of Dimitry Foures, Founding Member and Data Scientist

Which of the 8 UN Global Goals (Sustainable Development Goals) pre-selected for this competition does your solution relate most closely to? [select all that apply]

Affordable and Clean Energy, Climate Action.

Leadership and the Unilever Awards
Please provide examples of any previous entrepreneurial initiatives you have pioneered.

No prior entrepreneurial initiatives

Beyond your existing team, who else are you working with to achieve your objectives, eg partners, advisors, mentors?

We rely on a diverse and extremely knowledgeable set of advisors to achieve our objectives. Among these, we count:
Yohei Kiguchi–Current University of Cambridge PhD Candidate in Data Science; co-developer of TOU algorithm and co-author of paper to be published in December 2015 detailing research methodology behind TOU
Ruchi Choudhary–PhD advisor and faculty mentor from University of Cambridge
Jan Teichmann—Technical advisor and former data scientist at Cambridge Energy Data Lab
Ippei Arita—Current President at Enechange (Japan) and former executive at Cambridge Energy Data Lab
Atsuo Shiraki—Current CTO at Enechange (Japan) and former executive at Cambridge Energy Data Lab