Abstract: The emergence of the electricity spot market underscores the critical role of short-term electricity price forecasting for decision-makers in various market sectors. The increasing integration of clean energy and energy storage presents substantial challenges for short-term price predictions. This paper introduces a multi-step short-term electricity price forecasting model using the Maximum Information Coefficient (MIC), Ensemble Empirical Mode Decomposition (EEMD), and an enhanced Informer approach. Initially, MIC is applied to identify factors highly correlated with electricity prices, serving as the model's primary input sequences. These original sequences undergo EEMD decomposition, resulting in multiple Intrinsic Mode Functions (IMF) and a residual component. These components are then input separately into the improved Informer model to generate multi-step forecasts for the upcoming day, up to the 24th hour. The forecasted results undergo subsequent filtering. The filtered sequence forecast results are combined to produce the final prediction. Validation with data from the Spanish electricity market confirms that this model significantly improves the accuracy of short-term multi-step electricity price forecasting.