ENIAN’s mission is to provide insights and market intelligence for renewable energy professionals worldwide by delivering the data and workflow tools they need.
To achieve its aims, ENIAN applies the latest tools and techniques from data science and digital engineering packaged as robust, scalable and cost-effective software as a service which:
Data is essential to meeting our present and future energy needs, but it is “tedious data”:
Open Data holds promise, but faces major challenges because it is “tedious data”. That’s not because of its size, but rather its complexity, how it is published and how much work is needed to transform it into usable forms. To address parts of this, ENIAN won an Innovate UK grant to fund a collaborative project with the Data Lab at Edinburgh University.
By bringing together teams from academia and commerce, key challenges for the project were to address boundaries in geography, organisation and professional background, among others. To be successful, ENIAN understood it was important to decrease discontinuities and to develop continuities to improve the capability, effectiveness and output of the joint venture team.
The overall approach taken by Tilix was informed by its experience of working in both pure research and commercial software engineering. This holistic view helped the extended team deliver a scalable architecture and a minimal viable product.
The first role for Tilix was in overcoming some of the final hurdles needed to win the Innovate UK funding. Once the funds were approved the project moved into the elaboration phase, where the development team agreed sub-system boundaries and interfaces. Coming out of this process, tilix.energy constructed the infrastructure map data layer.
The Extraction, Transform and Load (ETL) system developed by tilix.energy extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a format so that application developers can build applications, data scientists can glean insights and decision making is supported.
ENIAN’s vision of AI-enabled applications that deliver increasingly refined results is driving the need for high-quality data to train the AI models. If the quality of that training data is not right, the performance of the AI models will not be satisfactory.
AI systems are a combination of statistical and mathematical techniques, an understanding of the world and data – lots of data. The more data that ENIAN has access to, and the better the quality, the more accurate its AI systems can be.
To wrap up the project, tilix.energy participated in a joint review of whole architecture. This included:
In supporting ENIAN, tilix.energy has reiterated that the data upon which algorithm is trained and operated is just as important. The idea that garbage in leads to garbage out is common sense. However, fully appreciating the role of data in both training and operating AI systems is pretty hard to do.
The support from Tilix was instrumental in getting ENIAN V1.0 to market. With the automated data collection and analysis of infrastructure data built into the ENIAN platform, renewable energy professionals are empowered to focus on getting quality deals done fast at low cost. Phil Bruner, CEO ENIAN
ENIAN’s passion for making energy data usable and affordable for every professional in the value chain has been stoked by the professional support from Tilix. They are helping us harness the power of big data and machine learning to generate serious time and cost savings for our customers. Varun Sharma, COO ENIAN
Tilix is a boutique management consultancy which supports energy industry technology vendors with a range of services including digital engineering and data science.
Tilix has a deep understanding of the energy value chain from generation to end use. Tilix’s experience includes solar, wind, battery storage, networks, energy retail, e-mobility and smart buildings.
Tilix’s expertise spans enterprise architecture, UX design, coding, testing and operations. The firm is digital first and are old hands in DevOps, machine learning and data science.