A novel combined probabilistic load forecasting system integrating hybrid quantile regression and knee improved multi-objective optimization strategy

In contemporary power systems, diverse energy integration and technological advancements have complicated load dynamics, revealing limitations in traditional deterministic forecasting techniques. Given the pivotal role of Probabilistic Load Forecasting (PLF) in mitigating risks in power system planning and operation, the pursuit of more accurate PLF techniques has evidently become a topic of paramount importance. Therefore, in this study, a novel hybrid Quantile Regression (QR) model integrating causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) is initially proposed for PLF, its core multi-scale feature extraction module can deeply mine the interactions between loads and their features. However, relying solely on a single-model often encounters inherent flaws in certain scenarios. Recognizing this, a Combined Probabilistic Load Forecasting System (CPLFS) is further proposed. This system incorporates the developed hybrid QR to establish a diversified candidate model pool, aiming to select the top-performing benchmarks in various scenarios and utilizes the Knee enhanced Pareto mechanism to adjust the combined weights of each benchmark, thereby bridging the gap created by the “No Free Lunch (NFL)” phenomenon. Taking the load uncertainty of Australia as an illustrative example, simulations indicate that the hybrid QR outperforms traditional models. Furthermore, CPLFS surpasses the best benchmarks and other combinations in both prediction accuracy and stability.