Localised high resolution forecasting for energy demand based on smart meter data

This is a PhD in Energy and Artificial Intelligence. Using smart meter data provided by the partner utility company, this PhD will investigate the applicability of bottom-up forecasting models for energy demand trained on historical data from individual smart meters.
This PhD will investigate, using smart meter data provided by the partner utility, how bottom-up forecasting models can be used to predict energy demand at a number of scales. These models will be trained on historical data from individual smart meters, and the forecasts will be aggregated at a local/regional level and compared with the accuracy of top down forecasts to assess what benefits exist and what level they occur.
Particular avenues of exploration could be:
- Spatial information allowing exploration of scenarios such as the impact of Locational Marginal Pricing (nodal pricing), a policy implemented in the United States and much discussed in the UK, on a utility based on its current portfolio of customers and the impact of the spatial distribution of its customers on profitability.
- the potential for balancing portfolios of customers, locally or within the suppliers base, and identifying locations of possible network constraints and substations likely to reach capacity with the uptake of electric vehicles.
- Design of an optimal trading agent for the hedging and purchasing of electricity given the portfolio of smart meter information is a likely final application of the research that would bring together a number of the individual strands of the project.
Methods:
As a starting point, state-of-the-art ensembling methods will be tested as a benchmark before exploration of the potential for deep learning methods in particular Long-Short-Term-Memory Neural Networks to improve results. Reinforcement learning and agent-based models will also be explored throughout the course of the project.

The low carbon energy systems needed to achieve the Government's carbon 2050 reduction targets promise declining generation costs, but at the price of inflexibility and intermittency. The challenge is to contain costs and improve energy system security, by building in resilience. The opportunities include: more efficient energy conversion, networks and storage technologies; improved energy control and management systems; integration of energy performance into modern methods of construction; improved measurement, display and control systems; and new business models. This will bring pervasive economic benefits: the creation of new intellectual property and expertise; businesses with the ability to compete in the huge new markets for energy efficiency and resilience, both in the UK and overseas; healthier and more productive places to work and live; and a means to address social hardship and inequalities, such as fuel poverty, which affects the health and wellbeing of society's most vulnerable. Seizing these opportunities requires leaders with multi-disciplinary knowledge, skills and whole-system perspective to break down restrictive, sector-specific silos, and drive innovation. The ERBE CDT will train such leaders.

The short and medium term impacts of the ERBE CDT will arise during the training of these leaders and through their research outputs and collaborations. These will include, but are not be restricted to: new approaches to analysis; new insights derived from large datasets; new modelling methods and ways of using existing models; new experimental techniques; field and laboratory measurement techniques; improved socio-technical methods; new manufacturing methods, devices, primary data sets, and patents; and, together with our industrial stakeholders, the integration of research into the business innovation process.

The longer term impacts will be realised over the next 40 years as ERBE graduates take on influential roles in diverse organisations, including:
- national and local governmental organisations that are developing affordable and socially acceptable evidence-based energy policies;
- energy supply and services companies that are charged with delivering a clean reliable and economical system, through deployment of energy efficiency products and technologies within an evolving energy system architecture;
- technology companies that are developing new components for energy generation and storage, new heating, cooling and ventilation systems, and smart digital controls and communications technology;
- industries that are large consumers of fuel and power and need to reduce their energy demand and curb the emission of greenhouse gases and pollutants;
- consultancies that advise on the design of energy systems, non-domestic building design and urban masterplans;
- facilities managers, especially those in large organisations such as retail giants, the NHS, and education, that are charged with reducing energy demand and operating costs to meet legally binding and organisational targets;
- standards organisations responsible for regulating the energy and buildings sectors through the creation of design guides and regulatory tools;
- NGOs and charities responsible for promoting, enabling and effecting energy demand reduction schemes;
- health and social care providers, who need to assure thermal comfort and indoor air quality, especially as our population ages and we adopt more flexible healthcare models.

The realisation of these benefits requires people with specific skills and an understanding of the associated ethical, health & safety, regulatory, legal, and social diversity and inclusion issues. Most importantly, they must have the ability to look at problems from a new perspective, to conceive, and develop new ideas, be able to navigate the RD&D pathway, and have the ability to articulate their intentions and to convince others of their worth; the ERBE CDT will develop these capabilities.