Understanding residential electricity consumers demand needs and renewable energy supply capabilities using explainable machine learning models
EU directives set out targets for renewable electricity, heat and transport. Low carbon technologies (LCTs) such as heat pumps (HPs) and electric vehicles (EVs) are components of the Government of Ireland’s Climate Action Plan to decarbonise heating and transport. The rate of adoption of LCTs and other technologies such as domestic photovoltaic (PV) generation is uncertain. The impact of geographic clustering, uncertainty in weather, and the range of technology options for EVs, HPs and PVs further complicate the evaluation of the impacts on the low voltage (LV) electricity distribution network. This poses considerable challenges to the management and operation of future distribution networks and smart grids. Similarly, renewable energy and LCTs have high potential to contribute to developing countries such as Uganda. These opportunities come with significant challenges. Currently only 1.1% of Uganda’s energy needs are served by electricity with 90% of total primary energy consumption in the form of firewood, charcoal, or crop residues. Solar offers potential in rural electrification schemes, while urban electricity networks are currently unreliable with regular load shedding. The Government of Uganda’s national plans aim to use renewable energy to support socio- and economic development in an environmentally sustainable manner. The challenges and opportunities in Uganda differ from those in Ireland, but are linked by the potential for Machine Learning to identify solutions and recommendations. This project responds to the need to identify how energy systems can be transformed to be secure (reliable), clean (green and sustainable), and fair (ensuring the citizen is at the centre of, and benefits from the transformed system). The project aims to use Machine Learning to support the evolving design demands of LV networks in urban areas, and to explore the potential for Machine Learning to support the development of renewable energy communities. It will focus on the impacts of LCTs, and the potential opportunities for local energy communities. The methodology and specific research questions will be defined in detail. Sample research themes include: ML to support (rural & solar) energy communities; ML to support LV network design; ML to support Smart Grid – fair load management in urban grids. The main deliverable will build on ML Fundamentals (particularly sequential data) to inform future LV electricity distribution network design decisions, and will address ML in Society to support fair operations of renewable energy communities.