Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia

Al-Musaylh, Mohanad S. and Deo, Ravinesh C. and Adamowski, Jan F. and Li, Yan (2019) Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia. Renewable and Sustainable Energy Reviews, 113 (Article 109293). ISSN 1364-0321

Abstract

Reliable models that can forecast energy demand (G) are needed to implement affordable and sustainable energy systems that promote energy security. In particular, accurate G models are required to monitor and forecast local electricity demand. However, G forecasting is a multivariate problem, and thus models must employ robust pattern recognition algorithms that can detect subtle variations in G due to causal factors, such as climate variables. Therefore, this study developed an artificial neural network (ANN) model that used climatic variables for 6-hour (h) and daily G forecasting. The input variables included the six most relevant climate variables from Scientific Information for Land Owners (SILO) and 51 Reanalysis variables obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) models. This information was used to forecast G data obtained from the energy utility (Energex) at 8 stations in southeast Queensland, Australia, by utilizing statistically significant lagged cross-correlations of G with its predictor variables. The developed ANN model was then benchmarked against multivariate adaptive regression spline (MARS), multiple linear regression (MLR), and autoregressive integrated moving average (ARIMA) models using various statistical metrics, such as relative root-mean square error (RRMSE%). Additionally, this study developed a hybrid ANN model by combining the forecasts of the ANN, MARS, and MLR models. The bootstrap (B) technique was also used with the hybrid ANN model, creating the B-hybrid ANN, to estimate the forecast uncertainty. According to both forecast horizons, the results indicated that the ANN model was more accurate than the ARIMA, MARS, and MLR models for G forecasting. Furthermore, the hybrid ANN was the most accurate model developed in this research study. For example, at the best site (Redcliffe), the hybrid ANN model generated an RRMSE of 3.85% and 4.37% for the 6-h and daily horizons, respectively. This study found that an ANN model could be used for accurately forecasting G over multiple horizons in southeast Queensland.


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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.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 11 Aug 2019 23:37
Last Modified: 18 Sep 2019 06:00
Uncontrolled Keywords: predictive model for electricity demand; climate and ECMWF Reanalysis variables; ANN; MARS; MLR; ARIMA; hybrid ANN; bootstrapping; sustainable energy management systems
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
URI: http://eprints.usq.edu.au/id/eprint/36877

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