“Value in Energy Data” is a series of seminars hosted by Digital and Data Consultant, Dr. Stephen Haben, and the Data Systems team at Energy Systems Catapult.
The Energy Data and Energy Digitalisation Taskforces have demonstrated the importance of digitalisation and data to the future modern energy system. New and disruptive technologies are reshaping how energy is generated, planned and managed and digital is the common and key enabler in all these areas.
The Data Systems team launched the “Value in Energy Data” seminar series during the coronavirus pandemic. Expanding beyond the original focus on data science, the series has covered everything from improving power grid operations, how open source tools help the utilisation of data, the importance of weather data to energy system modelling and many others.
These seminars aim to help bridge the gap across academia, industry and policy within the energy sector, a key objective of Energy Systems Catapult. We are very proud and privileged that the best and brightest from the world of data and digital agree to share their insights with us.
Professor Kyri Baker is an Assistant Professor at the University of Colorado Boulder, leading the GRIFFIN Lab, and is a Fellow of the Renewable and Sustainable Energy Institute (RASEI). She received her B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2009, 2010, and 2014, respectively. Her research focuses on renewable energy integration by changing the way the electric power grid operates.
ABSRACT: Optimization of electric power grids is a challenging, large-scale, non-convex problem. In order to optimize assets across these networks on fast operational timescales, the problem is typically simplified using linear models or other heuristics – resulting in increased cost of operation and potentially decreased reliability. Prof. Baker takes an alternate approach. Using the abundance of data generated by grid operators or generated offline to train machine learning models that can calculate optimal grid setpoints without even solving an optimization problem. – 10th Feb 2021
Prof. Hermann de Meer received his Ph.D. from University of Erlangen-Nuremberg, Germany, in 1992. He had been an Assistant Professor at Hamburg University, Germany, a Visiting Professor at Columbia University in New York City, USA, and a Reader at University College London, UK. Professor de Meer has been appointed as Full Professor at the University of Passau, Germany, and as Honorary Professor at University College London, UK, since 2003.
ABSRACT: Prof. de Meer’s research interests include cloud computing, energy systems, network virtualization, IT security, smart grid, smart city, industry 4.0, digitalization of energy systems, computer networks and communications, and distributed systems – 19th May 2021.
This special Value in Energy Data webinar focuses on showcasing the top 5 teams in the Presumed Open Data (POD) challenge. The POD project, led by Western Power Distribution, partnering with Energy System Catapult and Centre for Sustainable Energy, culminated in a data science challenge around WPD datasets.
PROJECT: Utilising PV generation data, demand data and weather variables, teams had to design a future battery schedule for a low voltage network which would both reduce evening peak demand and maximise the use of locally generated PV energy. This involved both designing an optimal control algorithm and developing forecasts to estimate demand and PV generation.
The challenge was spread over 7 weeks and include 5 tasks (including a practice task) and involved 55 teams of 142 individuals from 72 different institutions and 37 countries. – 16th June 2021
Dr Myriam Neaimeh is a Group Leader for the Data-Centric Engineering program at The Alan Turing Institute and Senior Research Associate at Newcastle University. Dr. Neaimeh secured over one million pounds to investigate Vehicle-to-Grid technology with the automotive and energy industry to roll-out EV charging infrastructure. Dr Matthew Deakin joined Newcastle University as a Research Associate in 2019 in the Power Systems engineering group. Dr. Deakin received the PhD in Engineering Science from University of Oxford in 2020, where he held a Clarendon Scholarship. His research interests include distribution network analysis and whole energy systems.
ABSRACT: As part of the e4future project and a Lloyd’s Register Foundation grant, they’re developing EVENT (Electric VEhicle Network analysis Tool) – an open access, cloud-based, tool that allows users to investigate impacts that may arise within electricity distribution networks due to the uptake of low carbon technologies such as EVs. EVENT builds and simulates a digital representation of electricity networks and allows users to change some parameters (e.g. size of transformer) and upload their own data (e.g. publicly available solar output data). – 28th July 2021
Ayrton Bourn is a PhD student at the UCL Energy Systems & AI Lab, an Energy Data Fellow with Climate Subak, and Director at Future Energy Associates. Ayrton has developed several open-source tools related to processing and analysing energy data with a wide range of users from academics to power traders. His research focuses on machine-learning approaches to wind power and market price forecasting.
ABSTRACT: Much progress around the digitisation of the energy sector, has focused on the crucial steps of standardising the language to describe datasets and assets. Ayrton covers some of the open-source tools being built that can leverage this common language to make existing workflows easier as well as open up entirely new approaches for energy data extraction and collation.
Ayrton introduces the different tools he’s involved in developing, including: wrappers (for retrieving data), dictionaries (for linking assets to datasets), mappers (for converting between metadata specifications), and harvesters (for automated data extraction). Including two specific examples – a wrapper for the Elexon data reporting service and a dictionary for GB power plants. He also discuss areas within the energy data ecosystem that are not receiving enough attention and missed opportunities in the context of automated energy data search, access, and extraction. – 25th August 2021
Dr. Hannah Bloomfield is a research scientist at the University of Bristol, who has spent the last eight years working at the University of Reading studying the impacts of climate variability and climate change on national-level power systems. She specialises in modelling UK and European electricity demand and renewable generation. A key outcome of her work has been to improve the accessibility of large meteorological datasets to non-specialists.
ABSRACT: Dr. Bloomfield explores why high quality weather and climate data is of key importance for energy system modelling, at timescales from a few days to multiple decades ahead. She draws on recent projects from the University of Reading energy-meteorology group and covers examples of open-access weather and climate datasets, highlighting the challenges in developing data across disciplines. – 13th October 2021
Robbie Morrison is an engineer from New Zealand now living in Berlin. He began working on high resolution energy systems models in 1995. In 2003, he added a GNU GPLv2+ open source license to the deeco codebase and tried to build a developer community. He joined the Open Energy Modelling Initiative (or openmod) in 2016, and began working on issues related to energy system data four years ago.
ABSTRACT: Suitably licensed open data together with relevant open standards can contribute to better public policy for the much-needed energy transition, through more open science and improved public interest analysis. Robbie covers the legal context for such data, the role of public licensing, the role of open standards, the need for a collective agreement on semantics and metadata in particular, and the benefits of community curation. The talk is limited to non-personal data that may be made public legitimately, thereby avoiding matters of personal and commercial privacy. – 19th January 2022
Miha Grabner is a researcher and lead data scientist in the Electric Power System and Operation department at Milan Vidmar Electric Power Research Institute (EIMV) in Slovenia. His main research field is Data Analytics and Machine Learning in Smart Grids, considering load and generation forecasting, smart meter data analytics, probabilistic load flow analyses, distributed generation integration, etc.
Miha is also an author of multiple studies and scientific papers and is responsible for managing AI projects. He is currently finishing his Ph.D., where his main research topic is forecasting large groups of electricity demand time series in distribution networks using Deep Learning.
ABSTRACT: Using energy data and the latest AI approaches can improve network planning and operation. Miha covers the main challenges in applying state-of-the-art approaches to real-world problems such as demand side management, load & generation forecasting, forecasting for conservation voltage reduction, consumer profiling for tariff design, etc.
Drawing on major AI projects from EIMV, Miha covers the consistent issues that need to be addressed in the energy industry before implementing data-driven Smart Grids. – 2nd March 2022
Dr. Zoya Pourmirza is Security and Resilience lead at the Centre for Energy at Newcastle University. She works with the National Centre for Energy Systems Integration (CESI) and Active Building Centre (ABC), investigating energy systems digitalisation with a focus on ICT design, data communication efficiency, and cybersecurity.
Dr. Pourmirza was appointed lead researcher for the UKRI Research and Innovation Infrastructure Roadmap that informed UK Government policymakers on 2050 carbon targets. Currently, she is the Principal Investigator for the ‘Energy Systems Digitalisation’ project working with international partners, and for the EPSRC IAA project ‘Digitalisation and Digital Twining of Energy Systems’ with Energy Systems Catapult.
ABSTRACT: Digitalisation supports decentralisation and decarbonisation of energy systems by enabling more penetration of distributed energy resources, and allowing services such as flexibility services, Demand Side Respond (DSR), and enhanced energy management. There also facing some technical challenges such as missing data and efficient data communication, and Cyber Security issues.
Dr. Pourmirza’s draws on Newcastle University projects on energy systems digitalisation, the opportunities, challenges and solutions. – 13th April 2022
Samuel Hinton is a senior data scientist working with Arenko. An experienced consultant with a background in data science, data engineering, astrophysical research, and software engineering. He is a lover of Bayesian statistics and time series forecasting, and maintains and contributes to numerous open-source libraries and provides courses in the area of data and statistics.
ABSTRACT: Samuel covers the frameworks, processing, and tooling that allows Arenko to download, sanitise, process, store, and utilise market data. Focus will be given on what open tools are available to streamline these tasks, and what the current pain points are in the energy industry. – 25th May 2022
Dr. Fei Teng is the Director of Education in Energy Futures Lab and a lecturer in the Department of Electrical and Electronic Engineering at Imperial College London.
He holds visiting Professor positions at MINES ParisTech, France and PloyU, Hong Kong.
Dr. Teng’s research focuses on the efficient, secure, privacy-preserving, and resilient operation of future cyber-physical energy systems. He has authored over 80 scientific publications in leading power system journals and conferences. His research has been funded by EPSRC, ESRC, Innovate UK, Royal Society, EDF Energy, and National Grid ESO.
ABSTRACT: There is a growing trend to utilise data and machine learning to facilitate the decarbonisation of our energy sector. However, one of the key challenges is to obtain a high-quality dataset for the various applications. In particular, some energy data may contain individual personal and/or confidential business information, which has raised significant concerns over privacy.
Dr. Teng highlights privacy challenges and emerging privacy-preserving techniques to promote data sharing for energy system applications. – 22nd June 2022
Dr. Tao Hong is Duke Energy Distinguished Professor, Graduate Director, and Research Director of Systems Engineering and Engineering Management Department, Director of BigDEAL (Big Data Energy Analytics Laboratory), and NCEMC Faculty Fellow of Energy Analytics. He is Director at Large of International Institute of Forecasters (IIF), General Chair of Global Energy Forecasting Competition (gefcom.org), the Founding and Past Chair of IEEE Working Group on Energy Forecasting, and Founding and Past Chair of IIF Section on Water, Energy and Environment (SWEET).
Dr. Hong currently serves as a Department Editor of IEEE Transactions on Engineering Management, Associate Editor of International Journal of Forecasting, and Associate Editor of Solar Energy. Dr. Hong received his B.Eng. in Automation from Tsinghua University in Beijing, and his PhD with co-majors in Operations Research and Electrical Engineering from North Carolina State University.
ABSTRACT: During the past three decades, a few notable forecasting competitions have led to several major breakthroughs in energy forecasting. Despite differences in themes and emphasis, a common feature of these competitions is on the authenticity of the data – real world data were provided to the contestants to solve practical problems.
Dr. Hong shares his experience with several large and small energy forecasting competitions. He discusses the motivations and challenges of organising such competitions, and how the outcomes benefit both industry and academia. He will also discuss his outlook of the future energy forecasting competitions. – 8th Sept 2022
David Sykes leads the data team at Octopus Energy Group, a group of companies driving energy system change through innovative customer propositions and technology. David built and now leads the global data function and is responsible for analytics, data engineering and data science across the group.
ABSTRACT: Learn more about how Octopus Energy is using data to improve their offering for over 3 million energy customers in 7 countries. They license their energy retail platform to over 10 million more and own and operate £4bn worth of renewable assets.
Dr Richard Dobson is the Business Leader for Digital at the Energy Systems Catapult where he supports teams of software developers, data scientists, digital consultants and operations specialists to deliver net zero centric digital innovation. Richard has led a range of data centric policy and innovation projects including the Energy Data and Energy Digitalisation Taskforces. Prior to joining the ESC Richard worked in the telecoms sector focusing on technology strategy and at King’s College London developing energy efficient algorithms.
ABSTRACT: This talk provided an overview of the key digital and data developments in the last few years and considered the innovation that they have unlocked. Richard went on to discuss the outstanding digital and data challenges that need to be addressed and highlighted some of the most promising initiatives underway across the sector and where more focus is required.
Mònica Aragüés Peñalba received the M.Sc. degree in industrial engineering (major in Electricity) in 2011 and her Ph.D. in Electrical Engineering in 2016, both from the School of Industrial Engineering of Barcelona of the Technical University of Catalonia, Barcelona, Spain. Since 2010, she belongs to Centre of Technological Innovation in Static Converters and Drives, in the Electrical Engineering Department of the UPC.
Since April 2018, she is Lecturer of the Electrical Engineering Department of the UPC (Serra Hunter Fellow). She has participated in industrial and research projects related to the grid integration of renewables (offshore wind and photovoltaics) at transmission and distribution level.
ABSTRACT: Electric grids are evolving towards smart grids, with a growing integration of renewable generation, electric vehicles and energy storage systems. They are enablers of energy decarbonisation, but also imply a more complex operation of the electrical system.
AI techniques can contribute to addressing some of the derived challenges. In this session you will learn more about a European project developing an Analytic Toolbox that interconnects data providers and service providers in an energy marketplace that offers AI-based solutions to improve the monitoring, operation, maintenance and planning of the electrical networks of distribution.
Dr Alison Halford is an Assistant Professor (Research) and Impact Lead at the Centre for Computational Science and Mathematical Modelling (CSM). Her research emphasises meaningful participation informed by an ethics of care to give voice to those traditionally excluded from public engagement and positions of authority.
As a feminist, transdisciplinary scholar, her work draws upon intersectionality to identify and address to what extent AI design and practices can reproduce or challenge systems of oppression, discrimination, and structural inequality.
Central to this approach is the inclusion of non-academic stakeholders as active participants in the research process. By engaging stakeholders as co-producers of knowledge, it acknowledges different ways of knowing to produce understanding that dismantles knowledge silos between different disciplines and across sectorial boundaries to develop new ways of thinking about real-world problems.
ABSTRACT: In this Value in Energy Data webinar, drawing upon interviews and focus groups with key actors in the energy data sector, Dr Alison Halford offers insights into how the energy industry is approaching opportunities and challenges in the transition towards the digital transformation of UK energy systems.
In particular, by asking how ethical frameworks and ethics are applied by those working with energy data, she will address questions around:
By contending ethics, alongside regulations, should be central to decision-making processes, design and deployment when working with data, Alison will explore the role, value and benefit of robust ethical frameworks.
In suggesting the need for a culture shift in data-intensive industries to develop best practice, she will lay out a roadmap on how to deliver ethical, fair, and inclusive data collection, analysis, visualisation, and decision-making within the energy sector.
Dr. Nagy is an assistant professor in the Department of Civil, Architectural, and Environmental Engineering at The University of Texas at Austin, directing the Intelligent Environments Laboratory since 2016. A roboticist turned building engineer, his research interests are in smart buildings and cities, renewable energy systems, control systems for zero emission building operation, and the application of machine learning and artificial intelligence for the built environment for a sustainable energy transition.
ABSTRACT: The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification.
To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response. However, residential demand response programs are hindered by the need for manual intervention by customers. To maximize the energy flexibility potential of residential buildings, an advanced control architecture is needed. Reinforcement learning (RL) is well-suited for the control of flexible resources as it is able to adapt to unique building characteristics compared to expert systems.
Yet, factors hindering the adoption of RL in real-world applications include its large data requirements for training, control security and generalizability. This talk will cover some of our recent work addressing these challenges.
Gavin helps solve complex, multidisciplinary, collective-action challenges. He has co-created over a dozen organisations: building multidisciplinary teams fit for a digital age to explore the impact of data on business, society and culture. He founded and now runs IcebreakerOne.org, making data work harder to deliver a Net Zero Future.
ABSTRACT:
Icebreaker One and Bankers for Net Zero (B4NZ) are developing Perseus, a pragmatic whole-of-market solution to create rapidly scalable, low-effort, low-friction sustainability reporting. This aims to help unlock access to capital by automating GHG reporting for every SME in the country.
Starting with SMEs, UK banks and energy companies, it will create a common framework and operational delivery of a solution that will begin to automate GHG reporting for every SME in the UK.
Perseus is backed by the UK Government with the initiative included in the Green Finance Strategy 2023. Further information on Perseus is available here.
Value in Energy Data #21: Despina Yiakoumi – Forecasting Contracts for Difference
Despina Yiakoumi is the Energy Analytics Manager at Low Carbon Contracts Company. Prior to this, she worked as a researcher in energy economics at the Cyprus Institute in Cyprus, as a Senior Lecturer at the University of Aberdeen and as a Transport Analyst at Energy Systems Catapult (ESC) in the UK. Previously, she pursued her PhD in Energy Economics at the University of Aberdeen on electricity market design with a focus on the auction design implemented as part of the GB capacity market. Despina has a strong interest in inclusion and diversity matters, energy market design, decarbonisation, as well as good coding and analysis practices. Currently, she is the Industry Liaison of the IEEE PES UK&I Women in Power Network.
ABSTRACT
The Contract for Difference (CfD) scheme is designed to support Great Britain’s transition to Net Zero by incentivising investments in new low-carbon electricity generation. The scheme is administered by the Low Carbon Contracts Company (LCCC), an arm’s-length body owned by the Secretary of State for the Department of Energy Security & Net Zero (DESNZ). Electricity suppliers are required, as per regulatory mandates, to contribute financial support to CfD payments facilitated by LCCC to electricity generators. This contribution is channelled through the CfD Supplier Obligation Levy for which LCCC is responsible to forecast. Key components to determine the Supplier Obligation Levy are electricity demand, renewable generation, and power prices for which LCCC provides forecast for a forward timeframe of 24 months.
Sarah Hayes is an independent consultant specialising in digitalisation and its impact across infrastructure sectors. She has worked on the National Digital Twin programme and is presently working on CReDo, the Climate Resilience Demonstrator with Connected Places Catapult.
Solomon Brown is a Professor of Process and Energy Systems at the University of Sheffield and the Director of EPSRC’s Centre for Doctoral Training in Energy Storage and its Applications. He has considerable expertise in analysis and design of clean energy processes and the study of energy systems.
Mark Workman is a specialist in Strategic Foresight. He uses mixed methods to develop insights about risks and opportunities in different possible futures. He works with corporates operating at the frontier of net zero.
ABSTRACT:
Consumers are leaving digital trails across a broader spectrum of their lives. These digital trails can be used to generate geo-spatial psychographic profiling. This can enable micro-targeting behavioral shifts in energy consumers.
The research undertaken mapped how data-driven psychographics can be applied to potentially influence consumer behaviour to meet Net Zero. The opportunities are substantial: Behavioural interventions in policy making; Energy System Flexibility and Stimulate Energy Sector Competition.
This is balanced by the potential exposure of consumers to risk of exploitation in a digitalised energy future. Some say that this is already too late. Cross-sectoral governance – i.e. beyond the energy sector – of data needs to be established to ensure that consumers are in a position to trust and be protected from malpractice.
Value in Energy Data #23: Rafał Weron – Recent Advances in Electricity Price Forecasting
Rafał Weron is Professor of Management Science and Head of the Department of Operations Research and Business Intelligence at the Wrocław University of Science and Technology. He is one of the leading world experts on energy forecasting and is periodically engaged as a consultant to financial, energy and software engineering companies. Details on current projects and publications are available on Rafał’s website.
ABSTRACT
Electricity Price Forecasting (EPF) is a branch of energy forecasting on the interface between econometrics/statistics, computer science and engineering, which focuses on predicting the spot and forward prices in wholesale electricity markets. Over the last 25 years, a variety of methods and ideas have been tried for EPF, with varying degrees of success. In this talk, Rafał will review recent developments in this fascinating area, including (but not limited to) probabilistic forecasting, combining forecasts and deep learning.
Value in Energy Data #24: Jonathan Farland – Generative AI across the energy industry
Jon Farland is Director of Solutions Engineering and a Senior Data Scientist at H2O.ai. His graduate research focused on applying machine learning techniques to hierarchical energy forecasting while working at ISO New England. After working at the ISO, Jon worked closely with utilities both in the US and internationally focusing on energy efficiency, demand response and distributed energy resources while working as a Senior Consultant at DNV, and Senior Data Scientist at TROVE Predictive Data Science. For the last decade, Jon has worked at the intersection of research, technology and energy with a focus on developing large scale and real-time predictive analytics models.
ABSTRACT
Over the last decade, there has been extensive global investment in “Smart Meter” technology used to meter both electricity and natural gas consumption at service points across entire distribution networks. This has led to an explosion of high-frequency time series data that can conceivably help manage demands, optimize behind-the-meter generation like solar, and even improve electrical load forecasting visibility and accuracy across the grid. Unfortunately, many of the regulators, rate payers and stakeholders of this investment are still asking the question: “how exactly can we use this new data?”.
However, Generative AI is now allowing us to combine these smart meter applications with data sources that have long been available; utility bills, work orders, wholesale market regulations, etc. Advanced Retrieval-Augmented Generation (RAG) systems enable us to converse with these documents, summarize them, and extract very granular pieces of data into much more meaningful forms. Paired with automatic machine learning (AutoML) and concepts like Feature Evolution and ML OPs, this unlocks an entire new world of understanding for those involved in the energy value chain.
This talk will introduce several advanced analytics use cases being deployed across the energy industry, as well as provide a live demo of how the H2O’s Generative AI can be used to rapidly develop AI applications and use cases.
Rebecca Duke is a principal data scientist at the STFC Hartree Centre, where she specialises in Energy Sector projects. She has led data science teams to deliver business change across a range of different industries including utilities and retail.
Dr Nam Nguyen is an AI Researcher at the STFC Hartree Centre. Before joining the Hartree Centre, Nam worked in academia at the University of Durham and the University of Edinburgh.
ABSTRACT
The work done in this project aims to explore the potential impact of controlling the UK Electricity Grid based on minimising carbon emissions. This splits into two parts:
Dr Alessandra Parisio is a Reader in the Department of Electrical and Electronic Engineering at The University of Manchester, UK, where she is/has been principal or co-investigator on research projects supported by EPSRC, Innovate UK, EC H2020 and industrial partners in the areas of building energy management and distributed control for flexibility service and grid support provision, totalling over £7 million as University of Manchester share.
ABSTRACT
The growing deployment of distributed energy resources can result in significant environmental and economic benefits but, at the same time, in reduced total system inertia and controllability, hence in new challenges to the power grid operation. Within this context, flexibility (i.e., the ability to adjust to the time-varying grid conditions) plays a crucial role for the transition towards power systems that can efficiently accommodate high shares of renewable energy sources. However, managing flexibility in urban districts and in distribution networks requires control and optimisation tools not yet available. Furthermore, there are several multi-energy systems within a district (i.e., systems with interconnected electricity/heating/gas networks), which currently lack coordination, and which can be regarded as excellent flexibility providers. Novel control strategies and schemes are needed to harness their unique potential. There is still a limited understanding of how to devise effective frameworks for coordinating an arbitrarily large number of flexibility sources. Filling this knowledge gap is essential for the transition to a more sustainable energy grid. In this talk, promising distributed control approaches for coordinating flexible resource, which leverage advanced methods, such as model predictive control and time-varying online optimisation, and data, are explored and illustrative case studies are discussed.
Hussain Kazmi is currently an assistant professor at KU Leuven, focusing on data science and decision support tools for the energy transition. He holds a PhD in electrical engineering from KU Leuven, MSc degrees in sustainable energy technology from Technical University of Eindhoven and Politecnico di Torino, and a B.E. in electrical engineering from National University of Sciences and Technology Pakistan.
ABSTRACT
Accurate predictive modelling of electricity demand and generation will become increasingly important in a renewable dominated power system. In case of mis-forecasts, similar models can also be used to help activate demand-side flexibility and provide essential grid services ranging from congestion and voltage management to frequency control. However, these models are not readily available for most flexible assets, due to the prohibitive costs of requiring domain experts to build them. The rise of smart meters means such models can increasingly be learnt using machine learning algorithms.
However, these models target predictive accuracy rather than learning correct causal relationships, which means they cannot be relied upon for downstream decision-making, even when post-hoc explainability tools are used.
In this talk, Hussain will discuss some of these issues in greater detail, and show how combining domain knowledge with observational data can lead to models that are not just accurate but also causally consistent with the help of several case studies.
Value in Energy Data 28: Centre for Net Zero – Value of Synthetic Energy Data for Decarbonisation
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