Short-term power load forecasting faces challenges such as multiple influencing factors, nonlinear and non-stationary load data, which make traditional prediction methods insufficient for engineering applications. To address this, this paper proposes a combined prediction model based on MPSR and IHBA-BiGRU-Attention. First, Spearman correlation coefficients are used to select power load influencing factors, forming a multivariate time series with load sequences; subsequently, multivariate phase space reconstruction (MPSR) is applied to this time series as input variables. An improved honey badger algorithm (IHBA) integrating Kent chaotic mapping, density factor modification and small-hole imaging opposite learning strategy is employed to optimize hyperparameters of the bidirectional gated recurrent unit (BiGRU) network, while the attention mechanism is applied to dynamically weight hidden state sequences of BiGRU, constructing a short-term power load prediction model based on MPSR and IHBA-BiGRU-Attention; finally, comparative experiments on power load datasets from Australia and Singapore showed mean absolute percentage errors of 0.650% and 1.067%, respectively, both lower than other models, validating its effectiveness. Additionally, predictive experiments on actual load datasets from a southern region in China achieved a mean absolute percentage error of 1.523%, outperforming competing models and demonstrating the engineering applicability of the proposed model.