Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
文献类型：期刊论文
| 作者 | Al-Musaylh, Mohanad S.1,2; Deo, Ravinesh C.1,4; Adarnowski, Jan F.3; Li, Yan1 |
| 刊名 | ADVANCED ENGINEERING INFORMATICS
 |
| 出版日期 | 2018 |
| 卷号 | 35页码:1-16 |
| ISSN号 | 1474-0346 |
| 关键词 | Electricity demand forecasting Machine learning SVR MARS ARIMA |
| DOI | 10.1016/j.aei.2017.11.002 |
| 通讯作者 | Al-Musaylh, Mohanad S.(MohanadShakirKhalid.AL-Musaylh@usq.edu.au) ; Deo, Ravinesh C.(ravinesh.deo@usq.edu.au) |
| 英文摘要 | 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 (RMSEG and MAE(G)), Willmott's Index (WI), Legates and McCabe Index (E-LM), and Nash-Sutcliffe coefficients (E-NS ) 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. |
| 收录类别 | SCI ; SSCI ; ISTP |
| WOS关键词 | SUPPORT VECTOR MACHINES ; ADAPTIVE REGRESSION SPLINES ; GLOBAL SOLAR-RADIATION ; NEURAL-NETWORKS ; ENERGY-CONSUMPTION ; PREDICTING RUNOFF ; PERFORMANCE ; PARAMETERS ; ALGORITHM ; PRICE |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000428494100001 |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2558147 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Al-Musaylh, Mohanad S.; Deo, Ravinesh C. |
| 作者单位 | 1.Univ Southern Queensland, Inst Agr & Environm IAg&E, Sch Agr Computat & Environm Sci, Toowoomba, Qld 4350, Australia 2.Southern Tech Univ, Management Tech Coll, Basrah, Iraq 3.McGill Univ, Fac Agr & Environm Sci, Dept Bioresource Engn, Quebec City, PQ H9X 3V9, Canada 4.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou, Gansu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Al-Musaylh, Mohanad S.,Deo, Ravinesh C.,Adarnowski, Jan F.,et al. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia[J]. ADVANCED ENGINEERING INFORMATICS,2018,35:1-16. |
| APA | Al-Musaylh, Mohanad S.,Deo, Ravinesh C.,Adarnowski, Jan F.,&Li, Yan.(2018).Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia.ADVANCED ENGINEERING INFORMATICS,35,1-16. |
| MLA | Al-Musaylh, Mohanad S.,et al."Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia".ADVANCED ENGINEERING INFORMATICS 35(2018):1-16. |
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来源：寒区旱区环境与工程研究所
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