A vector autoregression weather model for electricity supply and demand modeling

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.

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the US Electricity Sector

“…Restricted VAR models are powerful tools for multivariate time series analysis with complex correlations and have been widely adopted in econometrics 38 and electricity markets. 39 Compared with ordinary regression analysis, the restricted VAR model allows for dependencies between model variables that are too complex to be fully known. 38 Please refer to the Experimental Procedures section for the definition, Methods S1-S3 for details on the calibration and validation of the restricted VAR model, and Tables S1-S4 for the model parameters and results of statistical tests on the model.…”

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

“…Then, they model the residuals by means of a GARCH model. In [23], a Vector Autoregressive Model (VAR) is proposed to forecast temperature, solar radiation and wind speed of 61 locations around the United States. Recently, Cadenas et al [24] compared the predictive capacity of a univariate ARIMA model for wind speed against a nonlinear autoregressive exogenous model (NARX) model using as input the wind direction, temperature, pressure, solar radiation, relative humidity and speed of the wind.…”

Internet of Energy (IoE) and High-Renewables Electricity System Market Design

“…Wind and solar power are the fastest growing forms of renewable energy [55,56]. The supply of wind and solar energy is largely determined by wind speed and solar radiation which can be correlated only slightly with the times of electricity demand [57][58][59]. It is this feature of renewable energy intermittent power supply that adds cost to the entire generation system that is implicitly paid for by either other producers, consumers or taxpayers [60,61].…”