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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Residential Power and Battery Data

Authors: Bergmeir, Christoph; Bui, Quang; de Nijs, Frits; Stuckey, Peter;

Residential Power and Battery Data

Abstract

Overview The Residential Power and Battery data is an open-source dataset designed to facilitate the advancement of predictive and optimisation algorithms. It features anonymised, minute-by-minute real-world customer data on energy consumption, solar generation, and battery measurements. This dataset was compiled by SwitchDin and made available through Monash University on the Zenodo platform. Open-sourcing the Residential Power and Battery data offers numerous benefits to researchers, developers, and industry stakeholders. By providing access to comprehensive, real-world data on energy consumption, solar generation, and battery usage, the dataset enables the development of more accurate and efficient algorithms for energy management systems. For example, the dataset could facilitate the development of machine learning models that forecast energy consumption patterns, enabling better demand-side management strategies. These improved algorithms contribute to more effective demand response, grid stability, and renewable energy integration, helping to build a more resilient and sustainable energy future. Furthermore, open-sourcing this dataset fosters collaboration and knowledge sharing among researchers and professionals in the energy sector. By making the data freely available, researchers from various backgrounds and organisations can work together to identify patterns, trends, and innovative solutions to pressing challenges in energy management. This collaborative approach accelerates the pace of innovation, as diverse perspectives can generate novel ideas and methods that might not emerge in isolation. As a result, the open-sourcing of the Residential Power and Battery data has the potential to significantly advance the pursuit of a more efficient, reliable, and environmentally friendly energy landscape. Data Structure anonymous_public_power_data.rds utc: The date-time in UTC, formatted as yyyy-mm-dd hh:mm:ss. unit: A categorical label denoting the unique identifier for the unit. metric: A categorical label indicating whether the data point corresponds to load or solar power generation. max: A numerical variable denoting the peak value of load or solar power generation in kilowatts (kW) within a one-minute interval. anonymous_public_power_data_per_unit.zip Same as above, but files are split by units. anonymous_public_battery_data.rds unit: A categorical label denoting the unique identifier for the unit. batt_kwh: A numerical variable representing battery kilowatt-hour rating. batt_p_ch: A numerical variable representing battery charge power rating. batt_p_dch: A numerical variable representing battery discharge power rating.

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Keywords

Load, Solar Generation, Power, Energy, Time-Series, Battery Measures

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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