Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation

• Published in: Data in brief Vol. 47; p. 108985
• Main Authors: Alsalemi, Abdullah, Amira, Abbes, Malekmohamadi, Hossein, Diao, Kegong
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.04.2023; Elsevier
• Subjects: Data; Energy efficiency; Environmental sensing; Image processing; Internet of things; Occupancy; Smart plug; Visualization; Internet of things; Smart plug; Visualization; Image processing; Occupancy; Energy efficiency; Environmental sensing
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2023.108985

This data article describes a dataset collected in 2022 in a domestic household in the UK. The data provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plugs and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (°C), relative indoor humidity (RH%), and occupancy (binary). The dataset also includes outdoor weather conditions based on data from The Norwegian Meteorological Institute (MET Norway) including temperature (°C), outdoor humidity (RH%), barometric pressure (hPA), wind bearing (deg), and windspeed (m/s). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems.