The transportation sector accounts for 61.5% of global oil consumption and is responsible
for 29% of the world's total energy demand. Passenger transportation utilizes around 50 …

Occupant behaviour has significant impacts on the performance of machine learning
algorithms when predicting building energy consumption. Due to a variety of reasons (eg …

The transportation sector is deemed one of the primary sources of energy consumption and
greenhouse gases throughout the world. To realise and design sustainable transport, it is …

In recent decades, there has been a substantial rise in both worldwide energy consumption
and the accompanying increase in Carbon Dioxide (CO 2) emissions, primarily propelled by …

Abstract–Prediction of energy consumption in buildings is the process that allows estimating
the amount of energy consumed in a specific future period. Typically, a predicted energy …

Accurate energy consumption prediction is crucial for optimizing energy usage and
achieving sustainable development goals, especially in the field of building engineering …

Building energy consumption prediction is critical for building energy management and
energy policy formulation, and its inherent uncertainty can significantly affect the utilization of …

Artificial Intelligence methods (AI) have been widely applied in building energy consumption
prediction. As data-intensive methods, lacking sufficient input features will significantly …

This study compares missing value handling algorithms based on the type of missing value
occurrence for building monitored data. The study data comprised energy consumption and …

The urgency to combat climate change through decarbonization efforts is more crucial than
ever. The global building sector is one of the primary contributors to carbon emissions, yet …