Building energy demand response is projected to be important in decarbonizing energy use.
A demand response program that communicates “artificial” hourly price signals to workers …

Prosumers with generation and storage capabilities can supply energy back to the grid, or
trade their surplus with other prosumers for their mutual benefit. A prosumer aggregation that …

Reinforcement learning (RL) algorithms have proven to be useful tools for combinatorial
optimisation. However, they are still underutilised in facility layout problems (FLPs). At the …

Energy demand response is projected to be critically important in decarbonizing the grids of
the future. We propose an agent to communicate``artificial" price signals (ie, prices for each …

Personalized device-level energy consumption recommendations towards energy efficiency
can have a notable impact both on electricity bills and on the overall energy supply-demand …

Climate change is one of the most urgent problems faced by humanity, and rising sea levels,
extreme weather events and desertification pose a severe threat to human life as we know it …

Designing energy-efficient buildings is an essential necessity since buildings are
responsible for a significant proportion of energy consumption globally. This concern is even …

Demand response optimization and energy minimization in smart buildings have become
paramount due to the global rise in energy consumption and response to external signals …

Climate change requires a radical and complex transition in the way our energy sector
generates and uses energy. Solar and wind energy are leading carbon-free power sources …

As artificial intelligence (AI) rapidly advances, particularly in generative AI models and large
language models (LLMs), the educational landscape stands on the brink of transformation …