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:
Tue, 06/04/2019 - 00:10
DOI:
10.21227/jdw5-z996
Data Format:
License:
0
0 ratings - Please login to submit your rating.

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).