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 …

In recent years, numerous explainable artificial intelligence (XAI) use cases have been
developed, to solve numerous real problems in industrial applications while maintaining the …

Abstract Electric Load Forecasting (ELF) is the central instrument for planning and
controlling demand response programs, electricity trading, and consumption optimization …

Black-box models have demonstrated remarkable accuracy in forecasting building energy
loads. However, they usually lack interpretability and do not incorporate domain knowledge …

Addressing global energy and environmental crises requires transitioning to a renewable
energy system. Most system planning studies rely on historical power loads and some …

The tremendous rise of electrical energy demand worldwide has led to many problems
related to efficient use of electrical energy, consequently posing difficult challenges to …

In this paper, we present the concept and formulation of a short-term Markov corrector to an
underlying day-ahead building load forecasting model. The models and the correctors are …

Short-term electric power load forecasting is a critical and essential task for utilities in the
electric power industry for proper energy trading, which enables the independent system …

The investigation is based on the Taylor monetary rule usage on the basis of which a
decision about the key policy rate level is made (on data from Ukraine). An approach to …

Currently, machine learning methods are widely applied in the power industry to solve
various tasks, including short-term power consumption forecasting. However, the lack of …