Multi-step Short-Term Load Forecasting via Multi-population Genetic Programming

Short-Term load forecasting (STLF) is essential for the secure operation and effective planning of power systems. Accurate load forecasting not only ensures system reliability but also supports economic dispatch, optimal resource allocation, and risk management. However, due to the highly nonlinear and volatile nature of load patterns, as well as the influence of various external factors such as weather conditions, holidays, and human activities, single-step forecasting is often insufficient to meet practical requirements, making multi-step STLF particularly important for operational decision-making and planning. To address this challenge, this paper proposes a multi-population genetic programming (MPGP) approach to multi-step STLF. This approach evolves prediction models through multiple independent populations, with each population specifically trained and optimized for a fixed forecasting time step, thereby generating accurate predictions corresponding to each time step. Experimental results on real-world datasets demonstrate that the proposed approach can effectively capture complex load variation patterns, adapt to nonlinear dynamics, and maintain high accuracy across multiple steps, confirming the feasibility, robustness, and reliability of MPGP for multi-step STLF in power systems.