Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

Despite widespread adoption and outstanding performance, machine learning models are
considered as “black boxes”, since it is very difficult to understand how such models operate …

In recent years, machine learning, especially deep learning, has developed rapidly and has
shown remarkable performance in many tasks of the smart grid field. The representation …

Accurate probabilistic forecasting of photovoltaic (PV) power is crucial for optimizing energy
scheduling in smart buildings and ensuring the low-carbon, efficient operation of building …

Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a
promising research direction to intelligentize energy systems. With the massive smart meter …

Food security is currently a major concern due to the growing global population, the
exponential increase in food demand, the deterioration of soil quality, the occurrence of …

Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

This paper presents a new privacy-preserving framework for the short-term (multi-horizon)
probabilistic forecasting of nodal voltages in local energy communities. This task is indeed …

Quantifying short-term uncertainty in wind power plays a crucial role in power system
decision-making. In recent years, the scenario generation community has conducted …

Real-time electricity prices are economic signals incentivizing market players to support real-
time system balancing. These price signals typically switch between low-and high-price …