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The increasing deployment of Battery Energy Storage Systems (BESS) in modern electricity markets has introduced new complexities in system imbalance (SI) forecasting, particularly due to last-minute balancing actions by Balance Responsible Parties (BRPs). Conventional forecasting models, which primarily rely on historical imbalance patterns and exogenous market features, often fail to capture the dynamic corrective responses of BESS, leading to substantial prediction inaccuracies. This study systematically evaluates the impact of key battery parameters, including maximum power capacity ( P max ), depth of discharge ( D O D ) , and energy-to-power ( E / P ) ratio, on forecasting accuracy. A battery-aware autoregressive (AR) model is developed to explicitly integrate these factors, with predictive performance benchmarked against conventional models under both static and dynamic battery dispatch conditions. The analysis establishes well-defined operational stability constraints, demonstrating that forecast errors remain within considerably limits ( MAE ≤ 100 MW) when P max ≈ 416 MW, DOD ≤ 0 . 92 , and the E / P ratio is maintained either at E / P ≤ 4 . 64 or E / P ≥ 6 . 94 . However, within the intermediate range 4 . 64 < E / P < 6 . 94 , forecast errors exceed 100 MW, introducing instability and reducing predictive reliability. Notably, when P max is below 416 MW, variations in E / P and D O D exhibit minimal influence on forecast accuracy concerning the 100 MW MAE threshold. These findings underscore the intricate interdependencies among BESS parameters, highlighting the destabilizing effects of high-power dispatch, extended storage durations, and deep discharge cycles beyond these defined thresholds. Comparisons against the Elia forecast and a Naïve benchmark confirm that the battery-aware model enhances forecasting accuracy, improving MAE by up to 13.39%, RMSE by 20.61%, and sign accuracy by 12.06% over the Elia baseline. These improvements demonstrate the necessity for forecasting models that explicitly integrate battery dynamics to enhance predictive stability in evolving electricity markets. While this study employs an autoregressive framework for demonstration, the methodology and insights extend to advanced machine learning and probabilistic forecasting approaches. The findings provide actionable guidance for Transmission System Operators (TSOs) and market participants, offering a structured approach for optimizing imbalance management strategies, enhancing grid stability, and improving forecasting resilience in power systems with increasing BESS deployments. The graphical abstract illustrates a structured approach to system imbalance (SI) forecasting in the presence of Battery Energy Storage Systems (BESS). The process begins with dataset integration, incorporating SI alongside exogenous variables such as load, generation, and market data. These inputs undergo feature engineering, where autoregressive modeling, aggregated statistics, and time-based transformations refine predictive accuracy. A critical challenge arises from last-minute BESS reactions, where Balancing Responsible Parties (BRPs) dynamically respond to imbalance fluctuations. These rapid corrections can induce oscillatory behavior, leading to forecasting inaccuracies that exceed 500 MW, undermining grid stability insights. To address this, the forecasting model is enhanced by incorporating key battery parameters, including capacity, energy- to-power (E/P) ratio, maximum power (Pmax), and state of charge (SOC). This integration improves predictive performance, ensuring system imbalance forecast errors remain below 100 MW. This structured framework effectively demonstrates the role of BESS in stabilizing imbalance forecasting, highlighting the impact of battery-aware predictive modeling in enhancing grid reliability. • Developed a battery-aware AR model for system imbalance forecasting. • Integrated key BESS features (SOC, Pmax, DOD, E/P) to boost forecast accuracy. • Improved MAE by 13%, RMSE by 20%, and sign accuracy by 12% vs Elia. • Identified Pmax, E/P, DOD thresholds that ensure stable forecasts. • Results guide TSOs and BRPs in managing BESS for market balancing.