Smart grids are able to forecast customers' consumption patterns, ie, their energy demand,
and consequently electricity can be transmitted after taking into account the expected …

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 …

Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …

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) …

During this era of the energy crisis, when the non-renewable sources are rapidly
diminishing, efforts are being taken to utilize renewable sources predominantly. This …

The deep learning is a powerful tool for the short term load forecasting. The accurate load
forecasting is an inevitable task in power system for the proper planning of the electricity …

Electric energy consumption forecasting enables distribution system operators to perform
efficient energy management by flexibly engaging energy consumers under the intelligent …

In today's smart grid and building infrastructure, it is strongly suggested to implement short-
term demand forecasting for future power generation. There is a growing demand for …

The long-term demand forecast for annual national electricity and energy consumption plays
a vital role in future strategic planning, power system installation programming, energy …

For the problems of low accuracy and low efficiency of most load forecasting methods, a
load forecasting method based on improved deep learning in cloud computing environment …