Cox proportional hazards model used for predictive analysis of the energy consumption of healthcare buildings

González-Domínguez, Jaime; Sánchez-Barroso, Gonzalo; Neves, Nuno de Sousa

doi:10.1016/j.enbuild.2021.111784

Cited by 8 publications

(1 citation statement)

References 46 publications

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“…The advantage of the CPH model is that it does not need to make any specific probability distribution assumption for baseline survival time, so it has become one of the statistical methods widely used in survival analysis. 41 It is commonly used in research to quantify the degree of influence of covariable on equipment failure and the probability of survival event occurrence at each time point. 42 Harrell consistency index (C-index) 30 is used to compare the prediction errors of the two models, and the probability that the predicted results are consistent with the actual results is estimated.…”

Section: Case Studymentioning

confidence: 99%

RSF-based model for predicting pump failure trends in tunnels

Wu,

Chen,

et al. 2023

Advances in Mechanical Engineering

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The water pump is a piece of crucial electromechanical equipment to ensure the safety of tunnels. Therefore, it’s essential to master the performance trend of pumps to prevent the occurrence of failure. In this paper, essential information and failure records of pumps in 15 operating tunnels for many years were collected. According to the data characteristics, a data-filling model based on XGBoost is developed to address the issue of the censored data. Considering that most pumps are still in operation, a failure prediction model based on Random Survival Forest (RSF) is designed by incorporating survival analysis principles. The proposed Pump Failure Trend Prediction Model (PFTPM) overcomes difficulties caused by the lack of previous data and the small number of old pumps. We identify two phases of failure: the first phase exhibits a bathtub-shaped failure rate curve, while the second phase is characterized by a lower failure risk. The importance of considering rainfall, pump operating time, and performance changes for effective maintenance planning is emphasized. Furthermore, we summarize the failure evolution law of various types of pumps to amend maintenance cycle in the existing specification. Overall, this paper integrates innovative big-data technologies into the traditional maintenance data of tunnel pumps.

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Section: Case Studymentioning

confidence: 99%

RSF-based model for predicting pump failure trends in tunnels

Wu,

Chen,

et al. 2023

Advances in Mechanical Engineering

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A fuzzy cognitive map-based algorithm for predicting water consumption in Spanish healthcare centres

Sánchez-Barroso,

González-Domínguez,

Almeida-Fernandes

et al. 2023

Build. Simul.

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The management of water consumption in healthcare centres can have positive impacts on both the environmental performance and profitability of health systems. Computational tools assist in the decision-making process of managing the operation and maintenance of healthcare centres. This research aimed to integrate the empirical knowledge of experts in Healthcare Engineering and the historical data from 66 healthcare centres in a Fuzzy Cognitive Map. The outputs of the predictive model included water consumption, water cost, and CO2 emissions in healthcare facilities, along with eleven variables to discover the causes and consequences of water consumption in healthcare centres. A healthcare centre with about 12 350 users, located in a city that experiences an average of 1100 heating degree days, whose facilities be moderately energy-efficient contributing over 50% with renewable energies is expected to consume 8.4 dam3 of water with 32.1 k€ of cost, and contribute realising 30.8 ton CO2eq emissions. The use of Fuzzy Cognitive Maps for prediction can provide a high level of effectiveness in identifying the factors that contribute to water consumption and in designing key performance indicators to manage the environmental performance of healthcare buildings. This tool is extremely effective in enhancing the performance of the management division of health systems.

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