Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …

Abstract The application of Artificial Intelligence (AI) in environmental monitoring offers
accurate disaster forecasts, pollution source detection, and comprehensive air and water …

Buildings consume about half of the global electrical energy, and an accurate prediction of
their electricity consumption is crucial for building microgrids' efficient and reliable …

The present study aims to develop an integrated multivariate model based on variational
mode decomposition (VMD), convolutional neural network (CNN) and Bi-directional gated …

This paper presents an innovative univariate Deep LSTM-based Stacked Autoencoder
(DLSTM-SAE) model for short-term load forecasting, equipped with a Multi-Stage Attention …

With the introduction of various loads and dispersed production units to the system in recent
years, the significance of precise forecasting for short, long, and medium loads have already …

Demand prediction is playing a progressively important role in electricity management, and
is fundamental to the corresponding decision-making. Because of the high variability of the …

Accurate and efficient load forecasting is of great significance for stable operation and
scheduling of modern power systems. However, load data are usually nonlinear and non …

Accurate load forecasting can efficiently reduce the day-ahead dispatch stress of power
system or microgrid. The overview of load forecasting based on artificial intelligence models …

Accurate and reliable load forecasting can ensure the safety and economy of power system
operation. To improve the accuracy of short-term power load forecasting, this paper adopts …