A building energy model (BEM) is essential for understanding building energy consumption,
evaluating energy-saving measures, and develo** associated codes, standards, and …

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented
opportunities for a deeper understanding of occupant behaviour and energy usage patterns …

The paper proposes a multi-objective optimization approach to address the energy design of
the building envelope. A genetic algorithm (GA) is implemented by means of the coupling …

This paper provides practical guidelines to the Bayesian calibration of building energy
models using the probabilistic programming language Stan. While previous studies showed …

Increasing risks of energy security and greenhouse gas emission due to the growing
urbanization trend have prompted the need for urban energy demand prediction and …

Many efforts are used to eliminate the gaps between physical measurements and simulation
results. The Bayesian calibration technique is one of the popular automatic calibration …

Prediction of building energy use, when performed at urban scale, is influenced by the
choice of modelling approach, as well as the quality of available data. In the case of data …

Heating load prediction of district heating (DH) can give the heating demand, guide the
strategies formulation and avoid excessive heat. However, existing prediction models from …

The increasing interest in retrofitting of existing buildings is motivated by the need to make a
major contribution to enhancing building energy efficiency and reducing energy …

The cost optimal method (COM) as applied in the Energy Performance of Buildings Directive
(EPBD) uses “non-calibrated deterministic reference buildings (RBs)”. Such RBs are defined …