Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

Al-Musaylh, Mohanad S. and Deo, Ravinesh C. and Adamowski, Jan F. and Li, Yan (2018) Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Advanced Engineering Informatics, 35 (C). pp. 1-16. ISSN 1474-0346

Abstract

Accurate and reliable forecasting models for electricity demand (G) are critical in engineering applications. They assist renewable and conventional energy engineers, electricity providers, end-users, and government entities in addressing energy sustainability challenges for the National Electricity Market (NEM) in Australia, including the expansion of distribution networks, energy pricing, and policy development. In this study, data-driven techniques for forecasting short-term (24-h) G-data are adopted using 0.5 h, 1.0 h, and 24 h forecasting horizons. These techniques are based on the Multivariate Adaptive Regression Spline (MARS), Support Vector Regression (SVR), and Autoregressive Integrated Moving Average (ARIMA) models. This study is focused in Queensland, Australia’s second largest state, where end-user demand for energy continues to increase. To determine the MARS and SVR model inputs, the partial autocorrelation function is applied to historical (area aggregated) G data in the training period to discriminate the significant (lagged) inputs. On the other hand, single input G data is used to develop the univariate ARIMA model. The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models. The accuracy of the G forecasts, with respect to the measured G data, is assessed using statistical metrics such as the Pearson Product-Moment Correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Normalized model assessment metrics based on RMSE and MAE relative to observed means (View the MathML source), Willmott’s Index (WI ), Legates and McCabe Index (ELM), and Nash–Sutcliffe coefficients (ENS) are also utilised to assess the models’ preciseness. For the 0.5 h and 1.0 h short-term forecasting horizons, the MARS model outperforms the SVR and ARIMA models displaying the largest WI (0.993 and 0.990) and lowest MAE (45.363 and 86.502 MW), respectively. In contrast, the SVR model is superior to the MARS and ARIMA models for the daily (24 h) forecasting horizon demonstrating a greater WI (0.890) and MAE (162.363 MW). Therefore, the MARS and SVR models can be considered more suitable for short-term G forecasting in Queensland, Australia, when compared to the ARIMA model. Accordingly, they are useful scientific tools for further exploration of real-time electricity demand data forecasting.


Statistics for USQ ePrint 33425
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. This PhD study was supervised by Dr Ravinesh Deo (Environmental Modelling Simulation Group) as Principal Supervisor and A/Prof Yan Li as Associate Supervisor. The study was supported by Iraqi Government scholarship to the first author.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 06 Dec 2017 05:36
Last Modified: 08 Jan 2018 02:41
Uncontrolled Keywords: electricity demand forecasting; machine learning; SVR; MARS; ARIMA
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
09 Engineering > 0906 Electrical and Electronic Engineering > 090607 Power and Energy Systems Engineering (excl. Renewable Power)
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: 10.1016/j.aei.2017.11.002
URI: http://eprints.usq.edu.au/id/eprint/33425

Actions (login required)

View Item Archive Repository Staff Only