Bayesian Time-Series Analysis to Predict Real-Time Smart Grid Energy Price Uncertainty

被引:0
|
作者
Ahmadian, Saeed [1 ,2 ]
Ebrahimi, Saba [3 ]
Malki, Heidar [4 ]
机构
[1] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
[2] Afiniti, Houston, TX 77004 USA
[3] Univ Houston, Dept Ind Engn, Houston, TX 77204 USA
[4] Univ Houston, Dept Engn Technol, Houston, TX USA
来源
2021 IEEE 11TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2021年
关键词
Risk analysis; Uncertainty prediction; Hamiltonian Monte Carlo Markov Chain; Variational Bayesian Inference; Bayesian ARIMA; ARIMA;
D O I
10.1109/CCWC51732.2021.9375962
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The prediction of the real-time (RT) electricity prices in the smart grids is one of the challenging problems for electricity producers as well as consumers. To participate in smart grids RT electricity market, it is essential to predict the uncertainty inherent in price values caused by data collection and analysis methods and conduct risk analysis. In this paper, using the Auto-regressive Integrated Moving-Average (ARIMA) formulation of time-series, first a deterministic model of RT price is developed. Then, using Bayesian theorem and considering stochastic behavior of the prices, a stochastic ARIMA model to predict price uncertainty is presented. To obtain RT price posterior distribution, two different Bayesian statistical approaches are used and compared against each other. The first approach is direct Bayesian modeling using Hamilton Monte Carlo Markov Chain (HMCMC) sampling and the second method is Variational Bayesian (VB) Inference. Both methods are implemented on the New England Independent System Operator (ISO) public price dataset. The results confirm the solidness of proposed method to obtain price uncertainty and optimal confidence intervals.
引用
收藏
页码:517 / 523
页数:7
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