Parsimonious Modeling for Estimating Hospital Cooling Demand to Reduce Maintenance Costs and Power Consumption

Abstract

Hospitals are massive consumers of energy, and their cooling systems for HVAC and sanitary uses are particularly energy-intensive. Forecasting the thermal cooling demand of a hospital facility is a remarkable method for its potential to improve the energy efficiency of these buildings. A predictive model can help forecast the activity of water-cooled generators and improve the overall efficiency of the whole system. Therefore, power generation can be adapted to the real demand expected and adjusted accordingly. In addition, the maintenance costs related to power-generator breakdowns or ineffective starts and stops can be reduced. This article details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. This new operational method employed herein can be replicated in similar buildings with comparable water-cooled generators, regardless of whether the buildings are new or existing structures.