- Casual Inference and Graphical Models in Social Sciences
- Time Series Methods
- Big Data and Large Observational Datasets
- Modelling Heterogeneous Patterns of Behavior
- Missing Data and Attrition in Longitudinal Studies
- Innovations/challenges in the Teaching of Statistics in Social and Applied Sciences
Diprossimo, L, Ushakova, A, Zoski, J, Gamble, H, Irey, R, Cain, K, 2021, Vocabulary Scaffolding Features and Young Readers’ Comprehension of Digital Text: Insights from a Big Observational Dataset. Preregistration DOI:10.17605/OSF.IO/62C4Q
Speyer, L., Ushakova, A., Hall, A., Luciano, M., Auyeung, B., Murray, A. L., 2021, Analyzing Dynamic Change in Children’s Socio-Emotional Development using the Strengths and Difficulties Questionnaire in a large UK Longitudinal Study. preprint DOI: 10.31234/osf.io/wz9xc
Ushakova A., Taylor S.A., Killick R., 2021, Multilevel Changepoint Inference for Periodic Data Sequences. preprint DOI: 10.13140/RG.2.2.35838.51526
Murray, A., Ushakova, A. Bianchi, A., Booth, T., Lynn, P., 2021, Dynamic predictors of attrition in Understanding Society: a large 9-wave population-representative study.
Best, K. L., Murray, A.L., Speyer, L.G, Ushakova, A. 2021, Prediction of Attrition in Large Longitudinal Studies: Tree-based methods versus Multinomial Logistic Models. preprint DOI: 10.31235/osf.io/tyszr
Speyer, L. G., Hall, H. A., Ushakova, A., Luciano, M., Murray, A. L., & Auyeung, B. (2021). Dynamic Change in Socio-Emotional Development during Childhood and Adolescence , preprint DOI: https://doi.org/10.31234/osf.io/wn6pb
Ushakova, A., McKenzie, K., Hughes, C., Murray, A.,2021, Measurement Invariance of GHQ-12 Across Student and Non-Student Populations using a large UK Longitudinal Study.
Ushakova, A., McKenzie, K., Hughes, C., Murray, A., 2021, Analyzing Socio-Economic Risk Factors on Mental Health in Students as measured by GHQ-12 and WEMWBS: UK Understanding Society
Speyer, L., Hall, A., Ushakova, A., Murray, A. L., Luciano, M., Auyeung, B., 2021, Longitudinal Effects of Breastfeeding on Parent- and Teacher-Reported Child Behaviour. Archives of Disease in Childhood DOI: https://doi.org/10.1136/archdischild-2020-319038
Speyer, L., Hall, A., Ushakova, A., C., 2020, Links between Perinatal Risk Factors and Maternal Psychological Distress: Network Analysis. Acta Obstetricia et Gynecologica Scandinavica (AOGS) DOI: https://doi.org/10.1111/aogs.14056
Ushakova, A. and Mikhaylov,S.J., 2020, Big Data to the Rescue? Challenges in Analysing Granular Household Electricity Consumption in the United Kingdom. Energy Research and Social Science, Vol 64 .DOI: https://doi.org/10.1016/j.erss.2020.101428
Ushakova, A. and Murcio, R., 2018, . Interpreting Smart Meter Data of UK Domestic Energy Consumers. In Longley P., Cheshire J., Singleton A, Consumer Data Research (pp. 120-137), London: UCL Press. ISBN: 9781787353886
Ushakova, A. and Mikhaylov,S.J., 2018, Predicting Energy Customer Vulnerability from Consumption Behavior, Working Paper, Consumer Data Research Centre DOI:10.13140/RG.2.2.20553.19047
Overview of PhD Thesis work
Thesis Title: Generating Insights from Smart Meter Data: Challenges and Opportunities’
The introduction of smart meter technology has been central to recent innovations in energy provision for the UK residential sector. Smart meters have the potential to give greater insight into energy consumption behavior for energy providers and researchers alike. For example, they may aid our understanding of how the consumption of gas and electricity may be replaced by the energy from renewable sources, or how consumer behaviors can be changed to reduce overall energy consumption, increase efficiency, and lessen the pressure on the national grid networks. The advantage of a thorough understanding of the insights generated from smart meter data for policy issues may sound obvious at a first glance. However, there are significant challenges associated with the availability of methods and computation necessary to perform a complete analysis of the available data.
The thesis provides an in depth look at the nature of energy consumption through an analysis of big data that is recorded by around 400,000 smart meters installed at residential properties across the UK. It further discusses how this data is different from perhaps more conventionally collected retail consumer data, and in what way does the temporal nature of these data reveal information about the customers dynamics without compromising their anonymity. Various machine learning methods are applied and surveyed against more conventional methods often used by researchers and industry practitioners. Some extensions to improve the accuracy and reliability of methods for both segmentation of the behaviour, and prediction are also suggested. This latter point is particularly relevant if one is interested to forecast future energy use on both national and individual scale levels. Lastly, a case study looking at identifying the fuel poor from smart meter data is presented as an illustrative example of potential research questions one may answer with smart meter data records.