Prediction of Cooling Load of Tropical Buildings with Machine Learning

Abstract

Cooling load refers to the amount of energy to be removed from a space (or consumed) to bring that space to an acceptable temperature or to maintain the temperature of a space at an acceptable range. The study aimed to develop a series of models and determine the most accurate ones in the prediction of the cooling load of low-rise tropical buildings based on their basic architectural and structural characteristics. In this context, a series of machine learning (regression) algorithms were tested during the research to determine the most accurate/efficient prediction model. In this regard, a data set consisting of ten features indicating the basic characteristics of the building (floor area, aspect ratio, ceiling height, window material, external wall material, roof material, window wall ratio north faced, window wall ratio south faced, horizontal shading, orientation) were used to predict the cooling load of a low-rise tropical building. The dataset was generated utilizing a set of generative and algorithmic design tools. Following the dataset generation, a series of regression models were tested to find the most accurate model to predict the cooling load. The results of the tests with different algorithms revealed that the relationship between the predictor variables and cooling load could be efficiently modeled through Histogram Gradient Boosting and Stacking models.

Energy-efficiency measures to achieve zero energy buildings in tropical and humid climates

“…Among the local capacity building, the existing building orientation presents a limitation for the feasible measures applicable. Building orientation is a critical parameter for managing heat gains [61]; specifically, this parameter is among the significant predictors for cooling loads in tropical climates [62]. Moreover, double-glazed windows proved to be the most sustainable type for buildings, followed by plenum window type when evaluating their performance in tropical climates, based on energy consumption during building operation, global warming potential emission, embodied energy, and cost [59].…”

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

“…Kim et al [9] utilized Multi Linear Regression (MLR) and the Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF) algorithms to predict the electricity consumption of buildings, addressing temporal resolution and comparing algorithms to improve predictive accuracy. Bekdaş et al [10] utilized five foundational regression algorithms and five ensemble algorithms to predict cooling loads (the amount of energy that must be removed from or consumed in a space to keep its temperature at an acceptable level or within an acceptable range) based on the basic architectural and structural characteristics of low-rise buildings in the tropics and found that the Histogram Gradient Boosting algorithm and stacking models efficiently modeled the relationship between the predictors and cooling load. Matos et al [11] suggested a method to manage community energy balance through electricity consumption forecasts via eXtreme Gradient Boosting (XGBoost) and used a decision algorithm for energy trading with the public grid based on solar production and energy consumption forecasts, storage levels, and market electricity prices.…”

Leveraging digital technologies for circular economy in construction industry: a way forward

“…The construction industry plays a critical role in global economic development by contributing 13% of the gross domestic product (GDP) and providing 7% of the workforce worldwide (Elghaish et al, 2022;Bekdas ¸et al, 2023). Nonetheless, the construction industry is heavily reliant on the utilisation of natural resources, resulting in its status as a principal contributor to the degradation of the built environment (Naing et al, 2022;Omrany et al, 2021;Chang et al, 2022).…”

“…The construction industry plays a critical role in global economic development by contributing 13% of the gross domestic product (GDP) and providing 7% of the workforce worldwide (Elghaish et al. , 2022; Bekdaş et al. , 2023).…”