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

• Eduardo Dulce ^[1] ; Javier Martinez-de-Pison ^[1]
  1. [1] Universidad de La Rioja

     Universidad de La Rioja

     Logroño, España

     
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 181-192
• Idioma: inglés
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• Resumen

  • 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 watercooled 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.