The GECKO Team, as evidenced by academic publications and released public datasets, enabled by various training events, have made the following contributions so far:
The Information Science and Engineering Team:
- developed a novel framework for evaluating mathematical interpretability of deep 'black box' ML models and demonstrated how these can be used to determine the mechanism by which knowledge from one ML model can be effectively passed to another via knowledge distillation
- performed rigorous evaluation of what information can be mined at different smart meter sampling rates and how meaningful and trustworthy deep learning and domain-specific metrics are in CHT applications in ensuring ‘responsible’ and ‘ethical’, ‘bias-free’ outcomes
- demonstrated the challenges of transferability of non-intrusive load disaggregation to achieve net zero in agriculture by quantifying electricity consumption of various farming automation tools
- released a transformer-based deep learning software for load disaggregation, by processing entire sequences of data, understanding the significance of each part of the input sequence and assigning importance weights accordingly, to learn global dependencies in the sequence
-improved feature selection techniques for personal thermal comfort modelling
- tackle time series anomaly detection in smart homes
Social Science Team:
-Empirically, results have been achieved through in-depth qualitative methods such as interviews, workshops and observation with diverse communities including prosumers, smart home occupants, industry professionals, and other stakeholders. Major empirical advances have focussed on engaging diverse and often marginalised groups (e.g. island communities or energy vulnerable groups), and focussing on diverse CHTs across spatial scales rather than solely in the home.
-Methodologically, innovation has been demonstrated through detailed case study research on situated practices (e.g. in specific organisations or communities), with a focus on how practices are connected across scales (e.g. how domestic life connects with design imaginaries). Novel methodologies such as co-design and ‘walk with video’ methods have also been developed to engage and empower diverse groups.
-Conceptually, progress has been demonstrated through developments of social practice-based approaches, such as tracing connections between expert imaginaries and domestic practices, and combining practice theories with learning theory and design approaches. Understandings of ART AI and CHT have also been advanced through the development of more systemic and anticipatory approaches to social justice based on intersectionality.
-Expected results for the rest of the research centre on deepening these advances, and translating them into recommendations for improved design of CHTs, as well as more inclusive approaches to design that engage and empower diverse user groups.
Data Interpretation team:
-Demonstrated the challenges and needs involving smart users in the loop and optimising the AI/ML-models performance by reviewing literature and interviewing smart home users
-Demonstrated explainability for individual decisions by ML methods in different domains including energy demand forecasting in CHT, e-commerce, and personal thermal comfort prediction towards generating user-centered explanations.
-Implemented Graph-based neural networks that demonstrated high performance for load disaggregation providing trustworthy decisions.
-Demonstrated Deep Reinforcement Learning and Particle Swarm Optimization methods in residential Distributed Energy Resources scheduling and control.
