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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
From home energy management systems to energy communities: methods and data
Antonio Ruano1, Maria da Graça Ruano2
1IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, and Faculty of Science & Technology, University of Algarve, Faro, Portugal. aruano@ualg.pt.
The HEMStoEC database offers over three years of detailed energy consumption data from four Portuguese homes, including weather, solar, and appliance usage. This valuable dataset supports research in non-intrusive load monitoring and home energy management systems.
Area of Science:
- Energy Systems
- Building Science
- Data Science
Background:
- Accurate energy consumption data is crucial for developing effective home energy management systems (HEMS).
- Existing datasets often lack the granularity or comprehensive scope required for advanced HEMS research.
- The NILMforIHEM and HEMS2IEA projects generated extensive data on residential energy usage.
Purpose of the Study:
- To introduce and describe the HEMStoEC database, a novel resource for HEMS and non-intrusive load monitoring (NILM) research.
- To provide a comprehensive, multi-year dataset encompassing electrical consumption, environmental factors, and appliance operation.
- To facilitate research by offering both raw and processed data with detailed metadata.
Main Methods:
- Collected high-resolution (1-second and 1-minute) and synchronous (5-minute) data over three years (January 2020 - February 2023).
- Integrated data from multiple sources: household electricity consumption, local weather, photovoltaic and battery systems, indoor climate, and individual appliance usage.
- Organized the dataset monthly for improved manageability and accessibility, noting data gaps and interpolation periods.
Main Results:
- The HEMStoEC database contains over three years of synchronized data from four residential units in Southern Portugal.
- Data includes granular electrical consumption, weather conditions, solar energy generation and storage, indoor climate parameters, and specific appliance operation.
- The dataset provides raw and processed information, including identified data gaps and periods of data interpolation.
Conclusions:
- The HEMStoEC database represents a significant contribution to the field of home energy management and NILM.
- Its comprehensive nature and long duration make it ideal for training and validating energy modeling and prediction algorithms.
- Researchers can leverage this dataset to advance the understanding and optimization of residential energy efficiency and smart grid integration.
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