Heyfield Residential Load Profiles: Derived Dataset, 2021-2022

Description

This dataset presents typical load and sub-load profiles for a residential community in Heyfield, a rural town in Victoria, Australia. The dataset provides seasonal-mean daily (24-hour) load profiles for residential customers in Heyfield, derived from 107 Wattwatchers devices installed in 96 houses. The dataset includes information on energy consumption from 1 July 2021 to 30 June 2022 with hourly resolution. The typical residential profiles are derived for various categories based on household demographics and electrical asset ownership, including household size, rooftop solar PV, type of hot water system, type of space heating system, and the tariff connected to (day-rate vs controlled).

Steps to reproduce

The dataset was derived through a systematic process involving data collection, pre-processing, data classification, aggregation, and profile derivation. The initial step involved collecting energy consumption data from a sample of 96 customers over a twelve-month period. The collected data underwent pre-processing to eliminate errors and outliers, including data cleaning and imputation using standard techniques. Subsequently, the data was classified based on various household characteristics, such as occupancy, type of heating, type of water heating, whether the property has air conditioning (HVAC), and whether solar PV is installed. To derive the typical load profiles, the aggregated electricity consumption data was averaged over each hourly period for each month. To avoid small datasets, sub-load profiles were derived separately and then re-aggregated for the total household profile. Sub-load profiles were derived for different types of water heating, HVAC, and PV generation. The underlying household-level load, including appliances, any portable electric heating, and lighting, was also derived. The resulting dataset provides representative load profiles that reflect the average electricity consumption patterns of the Heyfield community, encompassing peak loads and periods of low energy usage. This dataset serves as a valuable resource for analysing local energy solutions, including microgrids, load flexibility, community batteries, energy efficiency upgrades, and community retailer/aggregator models. It can facilitate comparative studies and support decision-making processes in other similar rural communities seeking insights into residential electricity consumption patterns. In summary, this dataset offers researchers and stakeholders a resource of typical load and residential sub-load profiles for a community, enabling informed analysis and planning of sustainable energy solutions for rural communities.

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