DOI:10.1007/s40565-017-0365-1 |
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A vector autoregression weather model for electricity supplyand demand modeling |
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Author:
Yixian LIU1
, Matthew C. ROBERTS2
, Ramteen SIOSHANSI1
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Author Affiliation:
1. Department of Integrated Systems Engineering, The Ohio
State University, Columbus, OH, USA
2. Department of Agricultural, Environmental, and
Development Economics, The Ohio State University,
Columbus, OH, USA
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Foundation: |
The work presented in this paper was supported by the
National Science Foundation (No: 1029337). This work was also
supported by an allocation of computing time from the Ohio Supercomputer
Center. |
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Abstract: |
Weather forecasting is crucial to both the
demand and supply sides of electricity systems. Temperature
has a great effect on the demand side. Moreover, solar
and wind are very promising renewable energy sources and
are, thus, important on the supply side. In this paper, a large
vector autoregression (VAR) model is built to forecast
three important weather variables for 61 cities around the
United States. The three variables at all locations are
modeled as response variables. Lag terms are used to
capture the relationship between observations in adjacent
periods and daily and annual seasonality are modeled to
consider the correlation between the same periods in
adjacent days and years. We estimate the VAR model with
16 years of hourly historical data and use two additional
years of data for out-of-sample validation. Forecasts of up
to six-hours-ahead are generated with good forecasting
performance based on mean absolute error, root mean
square error, relative root mean square error, and skill
scores. Our VAR model gives forecasts with skill scores
that are more than double the skill scores of other forecasting
models in the literature. Our model also provides
forecasts that outperform persistence forecasts by between
6% and 80% in terms of mean absolute error. Our results
show that the proposed time series approach is appropriate
for very short-term forecasting of hourly solar radiation,
temperature, and wind speed. |
Keywords: |
Forecasting, Solar irradiance, Wind speed,
Temperature, Vector autoregression, Skill scores |
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Online Time:2018/07/20 |
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