Conventional models and artificial intelligence (AI)-based models have been widely applied
for energy consumption forecasting over the past decades. This paper reviews conventional …

Forecasting in the smart grid (SG) plays a vital role in maintaining the balance between
demand and supply of electricity, efficient energy management, better planning of energy …

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 …

Electric power, as an efficient and clean energy, has considerable importance in industries
and human lives. Electricity price is becoming increasingly crucial for balancing electricity …

Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging
and active research area. Forecasting about electricity load and price provides future trends …

With the rapid increase in the world's population, the global electricity demand has
increased drastically. Therefore, it is required to adopt efficient energy management …

The electrical load forecast is getting more and more important in recent years due to the
electricity market deregulation and integration of renewable resources. To overcome the …

Aggregation of distributed generations (DGs) along with energy storage systems (ESSs) and
controllable loads near power consumers has led to the concept of microgrids. However, the …

The global demand for electricity has visualized high growth with the rapid growth in
population and economy. It thus becomes necessary to efficiently distribute electricity to …