Artificial　intelligence　has　potential　for　forecasting　reactor　conditions　in　the　nuclear　industry.　Owing　to　economic　and　security　concerns,　a　common　method　is　to　train　data　generated　by　simulators.　However,　achieving　a　satisfactory　performance　in　practical　applications　is　difficult　because　simulators　imperfectly　emulate　reality.　To　bridge　this　gap,　we　propose　a　novel　framework　called　simulation-to-reality　domain　adaptation　(SRDA)　for　forecasting　the　operating　parameters　of　nuclear　reactors.　The　SRDA　model　employs　a　transformer-based　feature　extractor　to　capture　dynamic　characteristics　and　temporal　dependencies.　A　parameter　predictor　with　an　improved　logarithmic　loss　function　is　specifically　designed　to　adapt　to　varying　reactor　powers.　To　fuse　prior　reactor　knowledge　from　simulations　with　reality,　the　domain　discriminator　utilizes　an　adversarial　strategy　to　ensure　the　learning　of　deep　domain-invariant　features,　and　the　multiple　kernel　maximum　mean　discrepancy　minimizes　their　discrepancies.　Experiments　on　neutron　fluxes　and　temperatures　from　a　pressurized　water　reactor　illustrate　that　the　SRDA　model　surpasses　various　advanced　methods　in　terms　of　predictive　performance.　This　study　is　the　first　to　use　domain　adaptation　for　real-world　reactor　prediction　and　presents　a　feasible　solution　for　enhancing　the　transferability　and　generalizability　of　simulated　data.