Accurate load forecasting is challenging due to the significant uncertainty of load demand.
Deep reinforcement learning, which integrates the nonlinear fitting ability of deep learning …

Buildings are among the largest energy consumers in the world. As new technologies have
been developed, great advances have been made in buildings, turning conventional …

Abstract Empirical Modal Decomposition (EMD), and improved or modified techniques
derived from EMD, collectively referred to as Empirical Modal Decomposition class (EMDC) …

The accuracy of short-term load forecasting (STLF) is susceptible to the complex
components of original time series. Conventional data decomposition algorithms, such as …

Three gaps impede the development of cost-effective and accurate data-driven building
energy modeling/models (DBEM) for energy forecasting and predictive control strategies …

Monthly energy benchmarking supports identifying trends, improving energy efficiency, and
conducting cost management for building owners, managers, and policymakers better than …

Combined cooling, heating and power (CCHP) system, as a superior energy-provision form
of public building, is capable of achieving flexible and stable energy provision with high …

Smart meter (SM) deployment in the residential context provides a vast amount of data that
allows diagnose the behavior of household inhabitants. However, the conventional methods …

Accurate heat load forecasting is crucial for the high precise real-time operational control of
buildings in winter. The inconsistency of frequencies between features and heat load …

Accurate prediction of air conditioning load is the pivotal problem of air conditioning
optimization control, which is of great significance for reducing building energy consumption …