2020/09/28 by Kasun Chandrarathna, Chandrarathna, Kasun, Arman Edalati +3
Decision Sciences · Engineering · #Applications (stat.AP) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #FOS: Electrical engineering #Forecasting Techniques and Applications #Smart Grid and Power Systems #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.13595
openalex publication_date 2020/09/28 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
By significant improvements in modern electrical systems, planning for unit\ncommitment and power dispatching of them are two big concerns between the\nresearchers. Short-term load forecasting plays a significant role in planning\nand dispatching them. In recent years, numerous works have been done on\nShort-term load forecasting. Having an accurate model for predicting the load\ncan be beneficial for optimizing the electrical sources and protecting energy.\nSeveral models such as Artificial Intelligence and Statistics model have been\nused to improve the accuracy of load forecasting. Among the statistics models,\ntime series models show a great performance. In this paper, an Autoregressive\nintegrated moving average (SARIMA) - generalized autoregressive conditional\nheteroskedasticity (GARCH) model as a powerful tool for modeling the\nconditional mean and volatility of time series with the T-student Distribution\nis used to forecast electric load in short period of time. The attained model\nis compared with the ARIMA model with Normal Distribution. Finally, the\neffectiveness of the proposed approach is validated by applying real electric\nload data from the Electric Reliability Council of Texas (ERCOT). KEYWORDS:\nElectricity load, Forecasting, Econometrics Time Series Forecasting, SARIMA\n