Just, Adaptive and Meaningful (JAM): Energy Use Predictions for the Built Environment through Synthetic Data

This paper aims to develop a framework for generating synthetic data for the Swedish built environment to improve energy predictions and peak load management in heating systems. Heating and cooling are central aspects of reducing energy use in the residential sector. However, predicting the energy performance of buildings is currently a difficult task, partly due to energy use data from end-users being outdated and sometimes missing. In the end, we will contribute to future energy systems in a broader sense by developing a socio-technical and ethical methodological framework for working with synthetic energy data. We will map available data together with stakeholders' needs. Data will be collected and prepared as training data to develop and evaluate a model for synthetic data. The approach is interdisciplinary which will ensure the integration of socio-technical, ethical and gendered aspects of energy use and synthetic data.