A robust multi-step TBM attitude prediction approach named convolutional gated-recurrent-
unit neural network (C-GRU) is proposed in this research and the random balance design …

In digital era, energy efficient building remains a hot research topic because of increasing
concern regarding their environmental impact and energy consumption. Designing a …

The study presents a sophisticated hybrid machine learning methodology tailored for
predicting energy loads in occupied buildings. Leveraging eight pivotal input features …

Predicting cooling and heating loads is essential for efficient building energy management
in order to maintain indoor comfort. This study employs machine learning methods …

Heritage buildings are significant historical and architecture added value, which requires
deep and precise preliminary brainstorming when considering upgrading or retrofitting these …

After the sustainability of heritage buildings (HBs) has been evaluated and it is determined
that rehabilitation is needed, then the life-cycle cost (LCC) of energy for HBs can be …

Abstract Artificial Neural Networks are widely used in prediction activities and classification
processes. However, the implementation on average only uses a network architecture with …

Since the cooling systems used in buildings in hot climates account for a significant portion
of the energy consumption, it is very important for both economy and environment to …

This paper discusses the performance of three machine learning techniques namely
Decision Tree, Logistic Regression and Artificial Neural Network for predicting divorce …

Recently, metaheuristic algorithms have been recognized as applicable techniques for
solving various computational complexities in energy problems. In this work, a powerful …