Conference proceeding
Time-Series Forecasting Energy Loads: A Case Study in Texas
2022 Systems and Information Engineering Design Symposium (SIEDS), pp 196-201
28 Apr 2022
Abstract
Future predicted energy demand on the grid is a major factor that drives the prices of energy contracts on trading markets. Errors in forecasting are problematic for energy traders who buy and sell futures contracts on the expected price of energy: when decisions are made on inaccurate predictions, the market will be inefficient, leading to price volatility and investment losses. This paper proposes the use of an ensemble model of lasso and ridge regressions to predict energy loads. Specifically, the methodology is used to forecast hourly energy demand for up to forty-one hours in the future for the Electric Reliability Council of Texas (ERCOT). The features in the model include previous energy loads and time identifiers such as month, day, and hour of the prediction horizon. The methodology resulted in the creation of forty-one hourly models, each an ensemble of lasso and ridge regression models. The performance of the methodology is measured via out-of-sample data from ERCOT in 2020 against the ERCOT predictions for the same period.
Metrics
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4 citations in Scopus
Details
- Title
- Time-Series Forecasting Energy Loads: A Case Study in Texas
- Creators
- Rowan Rice - University of VirginiaKristina North - University of VirginiaGeoffrey Hansen - University of VirginiaDrew Pearson - University of VirginiaOliver Schaer - University of VirginiaThomas Sherman - CRCL Solutions,Austin,USADaniel Vassallo - CRCL Solutions,Austin,USA
- Publication Details
- 2022 Systems and Information Engineering Design Symposium (SIEDS), pp 196-201
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Scopus ID
- 2-s2.0-85134334342
- Other Identifier
- 991021861652304721