Forecasting Short-term load using Econometrics time series model with T-student Distribution

TL;DR

This paper proposes a SARIMA-GARCH model with T-student distribution for short-term electric load forecasting, demonstrating improved accuracy over traditional ARIMA models using real ERCOT data.

Contribution

It introduces a novel econometric model combining SARIMA and GARCH with T-student distribution for enhanced load forecasting accuracy.

Findings

The proposed model outperforms ARIMA with Normal distribution in accuracy.

Application to ERCOT data validates the model's effectiveness.

Model captures both mean and volatility of load data effectively.

Abstract

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