This study proposes a new approach for short-term power load forecasting using a
combination of convolutional neural networks (CNN), long short-term memory (LSTM), and …

Electricity is fundamental to the development of national economies and societies, reliant on
accurate power load forecasting for its stable supply. Ultra-short-term power load forecasting …

This paper presents the effectiveness of the ensemble empirical mode decomposition-long
short-term memory (EEMD-LSTM) model for short term load prediction. The prediction …

Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate
demand (in the next few hours to several days) for the power system. Various external …

Efficient estimation of the uncertainty associated with wind speed forecast is crucial for
evaluating wind farms' power quality and operation. Typically, the performance of prediction …

Accurate power load prediction is beneficial to the efficient use of electric energy and the
orderly development of power systems. Given the strong volatility and complexity of power …

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the
power system. Due to the strong fluctuation and complex characteristics of industrial loads, it …

Accurate power load forecasting enables Independent System Operators (ISOs) to precisely
quantify the demand patterns of users and achieve efficient management of the smart grid …

Accurate building energy prediction methods have become a key factor in achieving energy-
saving goals. Traditional methods for building energy multi-step prediction often use …

As the electric vehicle market continues to surge, the proper assessment of used batteries
has become increasingly important. However, current technologies for assessing used …