Every country must attain net-zero emissions, and the way to do so is through energy efficiency. According to a report released in September 2022 by the International Energy Agency in, electrification and energy efficiency are Indonesia's top goals for reaching NZE. Currently, cooling systems (chiller plants) account for more than 50 % of building energy use. Therefore, energy efficiency in chiller plant systems offers a high potential for achieving NZE and supporting the SDGs. This study seeks to identify a new algorithm control system for a building cooling system to decrease energy consumption of the building's chiller plant. A new algorithm will be developed based on predictive model with Deep Learning Neural Network Multi Output and Multi Stack Long Short-Term Memory. The developed algorithm will next be tested by running simulations with the model of Chiller Plant. Essential parameters are discovered using a matrix correlation. Based on the matrix correlation, Condenser Water System Temperature and Wet Bulb Temperature were revealed to be the most influential parameters affecting chiller plant performance. The proposed algorithm is able to optimize Chiller Plant with the result of alleviating the use of energy by 10.72 % with less error MSE, MAE, and RMSE respectively of 0.6527, 0.8079, and 0.8079. © 2025 IEEE.