Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

A vector autoregression weather model for electricity supply and demand modeling

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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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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    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.

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  • Online: July 20,2018
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