The prediction of residential building electricity consumption can help provide an early
warning regarding abnormal energy use and optimize energy supply. In this study, a …

Predicting electricity consumption is notably essential to provide a better management
decision and company strategy. This study presents a hybrid machine learning model by …

This paper compares the accuracy of energy prediction using Feedforward Neural Networks
(FNN) with a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) …

Smart grid technology based on renewable energy and energy storage systems are
attracting considerable attention towards energy crises. Accurate and reliable model for …

Background: Electricity consumption prediction plays an important role in conservation,
development, and future planning. Accurate prediction model has various field applications …

As a popular data driven method, artificial neural networks (ANNs) have been widely
applied in building energy prediction field for decades. To improve the short term prediction …

The implementation of the smart grid will greatly improve the efficiency of energy supply by
detecting, predicting, and reacting to real-time local changes of energy uses. To this end …

Electricity consumption forecasting has important implications for the mineral companies on
guiding quarterly work, normal power system operation, and the management. However …

Buildings are considered to be one of the world's largest consumers of energy. The
productive utilization of energy will spare the accessible energy assets for the following …

The rapid increase in human population and development in technology have sharply
raised power consumption in today's world. Since electricity is consumed simultaneously as …