The advent of new data-mining techniques and, more recently, swarm-based optimization
algorithms have antiquated traditional models in the field of energy performance analysis …

Today, energy conservation is more and more stressed as great amounts of energy are
being consumed for varying applications. This study aimed to evaluate the application of two …

The significance of accurate heating load (HL) approximation is the primary motivation of
this research to distinguish the most efficient predictive model among several neural …

A reliable prediction of sustainable energy consumption is key for designing environmentally
friendly buildings. In this study, three novel hybrid intelligent methods, namely the …

In this paper, the Multi-Layer Perceptron (MLP) neural network is optimized with two
metaheuristic algorithms, namely Artificial Bee Colony (ABC) and Particle Swarm …

Recent studies have witnessed remarkable merits of metaheuristic algorithms in
optimization problems. Due to the significance of the early analysis of the thermal load in …

This study pursues optima modification of heating, ventilating, and air conditioning (HVAC)
systems embedded in residential buildings through predicting heating load (HL) and cooling …

The energy consumption of the heating, ventilation, and air-conditioning systems is a big
concern of energy-efficient buildings in smart cities, especially for cooling and heating loads …

Revolutions in human activities and lifestyles result in a transition from conventional to
intelligent residential building infrastructure. Conventional heating, ventilation, and air …

Analyzing the thermal load is a significant task for energy-efficient buildings. Intelligent
models have shown high reliability for predicting the heating load of different buildings. But …