José Cecílio, Márcia Barros, and Alan Oliveira, LASIGE integrated researchers, published the paper “Leveraging Sustainable Household Energy and Environment Resources Management with Time-Series” in the top 10% journal Scientific Data. The paper was also co-authored by LASIGE’s former MSc student Tiago Rodrigues.

This research presents the SHEERM dataset, a novel and extensive dataset featuring comprehensive cross-sectional data from 13 households, spanning nearly three years of electrical load, energy cost, and on-premises solar energy production, all directly linked to solar irradiation and weather parameters. This dataset is essential for understanding and optimising energy utilisation, as well as for defining new policies to achieve the Sustainable Development Goals (SDGs) 7, 9, 11, and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation. The paper details the data collection process, including the sources and methodologies employed. Following established literature, the team developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, the paper offers additional results by applying machine-learning approaches to the provided data. This research aims to inform the design of new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition toward renewable energy and carbon neutrality.

The paper is available here.