Real-time energy management systems that are designed to support consumer supply and demand spectrums of electrical energy continue to face challenges with respect to designing accurate and reliable real-time forecasts due to the stochasticity of model construction data and the model’s inability to disseminate both the short- and the long-term electrical energy demand (G) predictions. Using real G data from Queensland, Australia’s second largest state, and employing the support vector regression (SVR) model integrated with an improved version of empirical mode decomposition with adaptive noise (ICEEMDAN) tool, this study aims to propose a novel hybrid model: ICEEMDAN-PSO-SVR. Optimization of the model’s weights and biases was performed using the particle swarm optimization (PSO) algorithm. ICEEMDAN was applied to improve the hybrid model’s forecasting accuracy, addressing non-linear and non-stationary issues in time series inputs by decomposing statistically significant historical G data into intrinsic mode functions (IMF) and a residual component. The ICEEMDAN-PSO-SVR model was then individually constructed to forecast IMFs and the residual datasets and the final G forecasts were obtained by aggregating the IMF and residual forecasted series. The performance of the ICEEMDAN-PSO-SVR technique was compared with alternative approaches: ICEEMDAN-multivariate adaptive regression spline (MARS) and ICEEMDAN-M5 model tree, as well as traditional modelling approaches: PSO-SVR, MARS and M5 model tree algorithms. To develop the models, data were partitioned into different subsets: training (70%), validation (15%), and testing (15%), and the tuned forecasting models with near global optimum solutions were applied and evaluated at multiple horizons: short-term (i.e., weekends, working days, whole weeks, and public holidays), and long-term (monthly). Statistical metrics including the root-mean square error (RMSE), mean absolute error (MAE) and their relative to observed means (RRMSE and MAPE), Willmott’s Index (WI), the Legat...