This paper presents a comprehensive review of the significant studies exploited Artificial
Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of …

Energy management systems in buildings (EMSs-in-Bs) play key roles in energy saving and
management to which an efficient energy management system in buildings (EMS-in-Bs) …

Controlling buildings energy consumption is a great practical significance. During early
design stage, accurate and rapid prediction of energy consumption could provide a …

Energy consumption of constructed educational facilities significantly impacts economic,
social and environment sustainable development. It contributes to approximately 37% of the …

Because of their low computational costs, surrogate models (SMs), also known as meta-
models, have attracted attention as simplified approximations of detailed simulations …

The aim of this study is to present a novel data-driven approach developed for space
heating energy demand calculation of the whole EU building stock. To develop a …

Over the last few decades, the construction sector's energy consumption has increased
tremendously. Buildings consume both embodied energy (EE) and operational energy (OE) …

Artificial neural network (ANN) has become an important method to model the nonlinear
relationships between weather conditions, building characteristics and its heat demand. Due …

The energy consumption of buildings accounts for a major share in the modern society.
Accurate forecast of building thermal demand is of great significance to both building …

The evaluation of building energy consumption is heavily based on building characteristics
and thus often deviates from the true consumption. Consequently, user-based estimation of …