Temporal resolution matters: a comparative life cycle assessment with energy balancing using real, annual, and disaggregated data

• Quantifies impact of temporal resolution on building LCA results. • Hourly disaggregation approximates monitored data within 3.4%. • Annual aggregation underestimates GWP by up to 42.8%. • Storage reduces bias but not under grid-connected conditions. • Temporal resolution is a decision-relevant LCA modelling choice. Life Cycle Assessment of buildings is used to support renovation and decarbonization decisions, yet it usually relies on annually aggregated operational energy data that neglect temporal variability in consumption, generation, and grid interactions. This study quantifies the influence of temporal resolution on LCA outcomes by comparing three operational data representations: (i) real monitored hourly electricity consumption and photovoltaic production, (ii) synthetic disaggregated hourly data reconstructed from annual values, and (iii) annual aggregated values. The analysis is applied to an office building in the Netherlands, assessed using a dynamic LCA framework developed within POCITYF project. Results demonstrate that, for the examined case study, the disaggregated hourly approach closely approximates the benchmark assessment based on monitored data, with deviations of 1.5% for primary energy consumption and 3.4% for global warming potential. In contrast, reliance on annual aggregated data leads to substantial underestimation, for the examined case study, of operational impacts, reaching 18.6% for primary energy consumption and 42.8% for global warming potential, due to the inability to capture intra-day and seasonal mismatches between electricity demand and on-site renewable generation. Increasing battery storage capacity reduces these discrepancies, but complete convergence is only achieved under near-isolated system conditions. The findings demonstrate that temporal resolution is shown to be a decision-relevant modelling choice in the context of buildings with dynamic operational profiles, rather than a secondary methodological detail. Annual aggregation can produce structurally biased results for grid-connected buildings with dynamic operational profiles, while disaggregated hourly data offer a promising alternative under conditions similar to those examined when high-resolution monitoring is unavailable. This work provides empirical evidence and practical guidance for improving the reliability of building LCA in renovation and positive energy district contexts.