With　the　development　of　intelligent　information　technology,　chiller　system　composed　by　different　interrelated　components　has　been　widely　used　in　industry　to　cool　products　and　machinery.　Predicting　the　status　of　chiller　system　can　effectively　monitor　energy　consumption　and　reduce　accident　rate.　In　this　paper,　we　propose　an　improved　LSTM　(E-LSTM)　method　to　predict　multi-component　chiller　status.　Firstly,　a　mean　filter　method　is　used　to　preprocess　the　original　multi-component　time　series　data.　Secondly,　we　adopt　E-LSTM　to　extract　hidden　features　from　seven　component-wise　inputs,　consisting　of　outdoor　temperature,　wet　bulb　temperature,　outdoor　enthalpy,　L1　&　L2　differential　pressures,　total　power,　and　IT　load.　Finally,　the　learned　hidden　features　are　fed　into　a　regression　layer　to　predict　three　future　chiller　statuses,　including　PUE,　cold　source　power,　and　refrigeration　secondary　pump　power,　respectively.　Experimental　results　show　that　the　proposed　method　outperforms　the　baselines,　such　as　linear　regression,　SVR,　RNN,　GRU　and　LSTM,　and　hence　demonstrate　the　effectiveness　of　our　proposed　method　in　the　task　of　chiller　status　prediction.　©　Springer　Nature　Singapore　Pte　Ltd.　2020.