The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand

Citation Author(s):
Yun
Li
Submitted by:
Yun Li
Last updated:
Mon, 07/15/2019 - 22:28
DOI:
10.21227/cmhx-8e70
Data Format:
License:
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Abstract 

This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019).  The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022).  The dataset is beneficial to various research such as long-term load forecast.

Instructions: 

The dataset contains two .CSV files. (1) The file “Arkana_daily_input_dat.csv” contains daily maximum load data with the average demand, customer count and PV capacity at Arkana substation. (2) The file “MUC_daily_input_dat.csv” contains daily maximum load data with the average demand, customer count and PV capacity at Muchea substation.