An advanced methodology to enhance energy efficiency in a hospital cooling-water system
Eduardo Dulce-Chamorro, Francisco Javier Martínez-de-Pisón
Abstract
Healthcare facilities consume massive amounts of energy. This study outlines a methodology to enhance energy efficiency and solve common problems in hospital cooling-water systems, since hospitals are the most energy-intensive type of building. Building Management Systems (BMS) are a widely used technique to control and monitor all the different energy facilities contained in hospitals. Proper setup and upgrades can resolve inefficiencies and existing problems. The methodology described herein addresses the general cooling system adjustments in three main areas: control system (CS), data acquisition system (DAS), and physical system (PS). An innovative feature incorporated in this methodology is the cooling demand model integrated into the CS, which is capable of forecasting and transmitting a schedule for maximum thermal energy requirements to the BMS a day in advance, thereby anticipating decisions and scheduling energy generation and maintenance operations. During the process of developing the cooling demand model, various machine learning models were trained. This process consisted of searching for low-complexity models using a methodology called GAparsimony. This methodology uses genetic algorithms to search for highly precise, robust models that use a low input. The final model consisted of a weighted combination of Artificial Neural Network (ANN) and Support Vector Regression (SVR) models. The energy savings obtained thanks to this methodology are estimated to be between 7% and 10% per year. The energy plant improved its performance and chiller starts were reduced by 82.5%. It should also be noted that this study was affected by the recommendations for increased ventilation due to the COVID-19 pandemic, which entailed at 22.4% increase in energy consumption in 2020. The methodology was developed and tested successfully in a real hospital BMS between 2017 and 2019; the model was finally integrated in 2020.