The rapid depletion of natural sources of energy, coupled with increasing global population
has triggered the emergence of various techniques and strategies for building energy …

Abstract Machine learning (ML) methods has recently contributed very well in the
advancement of the prediction models used for energy consumption. Such models highly …

Residential demand response is vital for the efficiency of power system. It has attracted
much attention from both academic and industry in recent years. Accurate short-term load …

An accurate electrical load forecasting is essential for optimal grid operation. The paper
presents a methodology for the short-term commercial building electrical load forecasting …

The prediction of building energy consumption plays a crucial role in building energy
management and conservation because it contributes to effective building operation, energy …

Precise and quick thermal load prediction for buildings is imperative in realizing the flexibility
of building energy systems. Operation optimization based on the prediction results can …

Reliable forecast of electricity demand is crucial to stability, supply, and management of
electricity grids. Short-term hourly and sub-hourly demand forecasts are difficult due to the …

Many factors affect short-term electric load, and the superposition of these factors leads to it
being non-linear and non-stationary. Separating different load components from the original …

The reliable and an economic operation of the power system rely on an accurate prediction
of short term load. In this paper, a deep learning based Long Short Term Memory (LSTM) …

To improve the accuracy and reliability of short-term power load forecasting and reduce the
difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN …