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

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

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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