论文标题
使用计量经济学时间序列模型预测短期负载，其T-Student分布
Forecasting Short-term load using Econometrics time series model with T-student Distribution
论文作者
论文摘要
通过对现代电气系统的重大改进，计划单位承诺和权力调度是研究人员之间的两个大问题。短期负载预测在计划和派遣它们中起着重要作用。近年来，在短期负载预测上已经完成了许多工作。拥有一个准确的模型来预测负载可能有益于优化电源和保护能量。诸如人工智能和统计模型之类的几种模型已用于提高负载预测的准确性。在统计模型中，时间序列模型表现出色。在本文中，一种自回旋的集成移动平均值（SARIMA） - 广义自动回归有条件异方差（GARCH）模型，作为对带有T -Student分布的有条件平均值和时间序列的波动性建模的有力工具，用于在短时间内预测电气负载。将获得的模型与具有正态分布的Arima模型进行比较。最后，通过应用得克萨斯州电动可靠性委员会（ERCOT）的真实电力负载数据来验证拟议方法的有效性。关键字：电力负载，预测，计量经济学时间序列预测，萨里玛
By significant improvements in modern electrical systems, planning for unit commitment and power dispatching of them are two big concerns between the researchers. Short-term load forecasting plays a significant role in planning and dispatching them. In recent years, numerous works have been done on Short-term load forecasting. Having an accurate model for predicting the load can be beneficial for optimizing the electrical sources and protecting energy. Several models such as Artificial Intelligence and Statistics model have been used to improve the accuracy of load forecasting. Among the statistics models, time series models show a great performance. In this paper, an Autoregressive integrated moving average (SARIMA) - generalized autoregressive conditional heteroskedasticity (GARCH) model as a powerful tool for modeling the conditional mean and volatility of time series with the T-student Distribution is used to forecast electric load in short period of time. The attained model is compared with the ARIMA model with Normal Distribution. Finally, the effectiveness of the proposed approach is validated by applying real electric load data from the Electric Reliability Council of Texas (ERCOT). KEYWORDS: Electricity load, Forecasting, Econometrics Time Series Forecasting, SARIMA
