Federated learning has recently emerged as a privacy-preserving distributed machine
learning approach. Federated learning enables collaborative training of multiple clients and …

Forecasting long-term energy consumption is essential to enhance resource utilization and
promote sustainability in campus buildings. This study employs a comprehensive approach …

Sufficient building operational data serve as the key premise to enable the development of
reliable data-driven technologies for building energy management. Considering that …

Efficient management of building energy consumption is paramount for sustainability and
cost-effectiveness, where anomalies in energy usage patterns can signify malfunctions …

Buildings' energy consumption forecasting is critical for energy saving and building
maintenance. However, most studies only focus on centralized learning of one dataset …

Building energy management acts as the brain of the building, which controls the energy
supply based on sensor data and algorithms. However, existing methods only focus on …

Accurate power consumption forecasting is crucial for optimizing energy use in smart
buildings, improving efficiency and decision-making to enhance overall energy …

As the complexity and diversity of power systems continue to increase, the economic
operation of these systems faces growing challenges. In this context, accurate and reliable …

Short term power load forecasting can accurately evaluate the overall power load changes
and provide accurate reference for power system operation decision-making. To address the …

Short-term load forecasting (STLF) plays a pivotal role in operational efficiency of power
plants. Leveraging data from utility companies for STLF in a wholesale market presents …