Estimating residential buildings’ energy usage utilising a combination of Teaching–Learning–Based Optimization (TLBO) method with conventional prediction techniques

Zheng, Senlin; Xu, Haodong; Mukhtar, Azfarizal; Hizam Md Yasir, Ahmad Shah; Khalilpoor, Nima

doi:10.1080/19942060.2023.2276347

Cited by 1 publication

(2 citation statements)

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“…The estimation of heating and cooling loads depends on the characteristics of the structure. To construct energy-efficient buildings, it is helpful to develop conceptual systems that anticipate the cooling load in the residential building sector [6].…”

Section: Introductionmentioning

confidence: 99%

“…(1) A critical review of the application of machine learning methods for the prediction of heating and cooling loads; (2) The introduction of a novel algorithm called the Multi-Objective Plum Tree Algorithm (MOPTA) which adapts the original Plum Tree Algorithm [21] to multi-objective optimization problems; (3) The ranking of the solutions using the MOORA method [22]; (4) The adaptation of the MOPTA to the hyperparameter optimization and the optimal regressor selection for a machine learning methodology used to predict heating and cooling loads, using the Energy Efficiency Dataset of the UCI Machine Learning Repository as experimental support [23,24]; (5) The development of an objective function that considers the averages of the heating and cooling RMSE results; (6) The comparison and validation of the obtained results with the ones obtained by the Multi-Objective Grey Wolf Optimizer (MOGWO) [25], Multi-Objective Particle Swarm Optimization (MOPSO) [26], and NSGA-II [27].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction

Slowik,

Moldovan

2024

Energies

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The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many of the studies considered the default values of the hyperparameters. This manuscript addresses both the selection of the best regressor and the tuning of the hyperparameter values using a novel nature-inspired algorithm, namely, the Multi-Objective Plum Tree Algorithm. The two objectives that were optimized were the averages of the heating and cooling predictions. The three algorithms that were compared were the Extra Trees Regressor, the Gradient Boosting Regressor, and the Random Forest Regressor of the sklearn machine learning Python library. We considered five hyperparameters which were configurable for each of the three regressors. The solutions were ranked using the MOORA method. The Multi-Objective Plum Tree Algorithm returned a root mean square error value for heating equal to 0.035719 and a root mean square error for cooling equal to 0.076197. The results are comparable to the ones obtained using standard multi-objective algorithms such as the Multi-Objective Grey Wolf Optimizer, Multi-Objective Particle Swarm Optimization, and NSGA-II. The results are also performant concerning the previous studies, which considered the same experimental dataset.

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Section: Introductionmentioning

confidence: 99%