Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining

The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that utilizes the available chiller data. The data mining techniques used are prediction model, clustering analysis, and association rules mining (ARM) analysis. The dataset was collected every minute for a year from a water-cooled chiller at an institutional building in Taiwan and from meteorological data. The power consumption prediction model was built using deep neural networks with 0.955 of R2, 4.470 of MAE, and 6.716 of RMSE. Clustering analysis was performed using the k-means algorithm and ARM analysis was performed using Apriori algorithm. Each cluster identifies those operational parameters that have strong association rules with high performance. The operational parameters from ARM were simulated using the prediction model. The simulation result shows that the ARM operational parameters can successfully save the energy consumption by 22.36 MWh or 18.17% in a year.