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

Date

2023-02-13

Advisors

Journal Title

Journal ISSN

ISSN

2352-3409

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

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 from the data 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 plus 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). 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.

Description

open access article

Keywords

Energy efficiency, internet of things, environmental sensing, occupancy, smart plug, signal/image processing, visualization

Citation

Alsalemi, A., Amira, A., Malemohamadi, H. and Diao, K. (2023) Novel domestic building energy consumption dataset: 1D time series and 2D Gramian Angular Fields representation. Data in Brief, 17, 108985

Rights

Research Institute

Institute of Energy and Sustainable Development (IESD)