Short-term load forecasting data with hierarchical advanced metering infrastructure and weather features
- Citation Author(s):
-
Jie Zhang (University of Texas at Dallas)Cong Feng (University of Texas at Dallas)
- Submitted by:
- Jie Zhang
- Last updated:
- DOI:
- 10.21227/jdw5-z996
- Data Format:
- Research Article Link:
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Abstract
Accurate short-term load forecasting (STLF) plays an increasingly important role in reliable and economical power system operations. This dataset contains The University of Texas at Dallas (UTD) campus load data with 13 buildings, together with 20 weather and calendar features. The dataset spans from 01/01/2014 to 12/31/2015 with an hourly resolution. The dataset is beneficial to various research such as STLF.
Instructions:
The dataset contains two .CSV files.
(1) The file “UTD_weather.csv” contains two-year hourly UTD weather data with 20 weather and calendar features.
(2) The weather data is obtained from the National Solar Radiation Database (NSRDB).
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