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 …

Accurate forecasting of cooling and heating loads is critical for optimizing the energy usage
of devices and planning for energy storage in building district energy systems (BDESs). Data …

Buildings and their energy systems have unique characteristics and temporally changing
dynamics. Additionally, building data have strong seasonality. Therefore, a deep learning …

District heat load forecasting is a challenging task that involves predicting future heat
demand based on historical data and various influencing factors. Accurate forecasting is …

This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of
buildings at individual and district levels in the early design stage. Seven input variables …

Meeting the goal of zero emissions in the energy sector by 2050 requires accurate
prediction of energy consumption, which is increasingly important. However, conventional …

Building energy consumption accounts for about 40% of global primary energy use and 30%
of worldwide greenhouse gas (GHG) emissions. Among the energy-related factors present …

Accurate building cooling load prediction is beneficial in managing optimal operation to
conserve energy user and operational cost. Several physics-based and data-driven models …

Data driven-based building energy load prediction is of great value for building energy
management tasks such as fault diagnosis and optimal control. However, there are two …

Building thermal load prediction is of great significance for energy conservation in HVAC
systems. Due to the visible and complicated time delay between influencing factors and …