A Hybrid Regression Model for Day-Ahead Energy Price Forecasting

• Published in: IEEE access 2019, Vol.7, p.36833-36842
• Main Authors: Bissing, Daniel, Klein, Michael T., Chinnathambi, Radhakrishnan Angamuthu, Selvaraj, Daisy Flora, Ranganathan, Prakash
• Format: Article
• Language: English
• Subjects: ARIMA; Autoregressive models; Autoregressive processes; Economic conditions; Economic forecasting; Electric power systems; Electricity; Electricity pricing; energy price; Forecasting; Holt-Winters; hybrid model and regression; Linear regression; Mathematical model; Power systems; Predictive models; Regression models; Statistical analysis; Statistical models; Support vector machines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2904432

Accurate forecast of the hourly spot price of electricity plays a vital role in energy trading decisions. However, due to the complex nature of the power system, coupled with the involvement of multi-variable, the spot prices are volatile and often difficult to forecast. Traditional statistical models have limitations in improving forecasting accuracies and reliably quantifying the spot electricity price under uncertain market conditions. This paper presents a hybrid model that combines the results from multiple linear regression (MLR) model with an auto-regressive integrated moving average (ARIMA) and Holt-Winters models for better forecasts. The proposed method is tested for the Iberian electricity market data set by forecasting the hourly day-ahead spot price with dataset duration of 7, 14, 30, 90, and 180 days. The results indicate that the hybrid model outperforms the benchmark models and offers promising results under most of the testing scenarios.