Predicting the household power consumption using CNN-LSTM hybrid networks
Prediction of power consumption is an integral part of the operation and planning of the
electricity supply company. In terms of power supply and demand, For the stable supply of …
electricity supply company. In terms of power supply and demand, For the stable supply of …
Predicting residential energy consumption using CNN-LSTM neural networks
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 …
raised power consumption in today's world. Since electricity is consumed simultaneously as …
Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks
Excessive Power Consumption (PC) and demand for power is increasing on a daily basis,
due to advancements in technology, the rise in electricity-dependent machinery, and the …
due to advancements in technology, the rise in electricity-dependent machinery, and the …
[HTML][HTML] Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data
J Chung, B Jang - PloS one, 2022 - journals.plos.org
The stress placed on global power supply systems by the growing demand for electricity has
been steadily increasing in recent years. Thus, accurate forecasting of energy demand and …
been steadily increasing in recent years. Thus, accurate forecasting of energy demand and …
Particle swarm optimization-based CNN-LSTM networks for forecasting energy consumption
Recently, there have been many attempts to predict residential energy consumption using
artificial neural networks. The optimization of these neural networks depends on the trial and …
artificial neural networks. The optimization of these neural networks depends on the trial and …
Deep learning based ensemble method for household energy demand forecasting of smart home
Electricity/Energy demand forecasting enables efficient electricity distribution through the
use of smart grid. For the construction of such devices, we need to equip our homes with …
use of smart grid. For the construction of such devices, we need to equip our homes with …
Predicting electricity consumption using deep recurrent neural networks
Electricity consumption has increased exponentially during the past few decades. This
increase is heavily burdening the electricity distributors. Therefore, predicting the future …
increase is heavily burdening the electricity distributors. Therefore, predicting the future …
LSTM based short-term electricity consumption forecast with daily load profile sequences
For energy-related services and researches, not only the energy load data in the past but
also the future are essential. In this paper, a short-term electricity consumption prediction …
also the future are essential. In this paper, a short-term electricity consumption prediction …
[HTML][HTML] Multi-step short-term power consumption forecasting with a hybrid deep learning strategy
Electric power consumption short-term forecasting for individual households is an important
and challenging topic in the fields of AI-enhanced energy saving, smart grid planning …
and challenging topic in the fields of AI-enhanced energy saving, smart grid planning …
Spatial granularity analysis on electricity consumption prediction using LSTM recurrent neural network
The building sector takes a large proportion of electricity consumption and carbon emission
in high-density urban areas. To reduce the carbon emissions and use energy more …
in high-density urban areas. To reduce the carbon emissions and use energy more …