Short-term electric load forecasting (STLF) plays a pivotal role in modern power system management, bolstering forecasting accuracy and efficiency. This enhancement assists power utilities in formulating robust operational strategies, consequently fostering economic and social advantages within the systems. Existing methods employed for STLF either exhibit poor forecasting performance or require longer computational time. To address these challenges, this paper introduces a hybrid learning approach comprising variational mode decomposition (VMD) and random vector functional link network (RVFL). The RVFL network, serving as a universal approximator, showcases remarkable accuracy and fast computation, owing to the randomly generated weights connecting input and hidden layers. Additionally, the direct links between hidden and output layers, combined with the availability of a closed-form solution for parameter computation, further contribute to its efficiency. The effectiveness of the proposed VMD-RVFL was assessed using electric load datasets obtained from the Australian Energy Market Operator (AEMO). Moreover, the effectiveness of the proposed method is demonstrated by comparing it with existing benchmark forecasting methods using two performance indices such as root mean square error (RMSE) and mean absolute percentage error (MAPE). As a result, our proposed method requires less computational time and yielded accurate and robust prediction performance when compared with existing methods.

The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.