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 …
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 household electric power consumption using multi-step time series with convolutional LSTM
Energy consumption prediction has become an integral part of a smart and sustainable
environment. With future demand forecasts, energy production and distribution can be …
environment. With future demand forecasts, energy production and distribution can be …
[HTML][HTML] Improving electric energy consumption prediction using CNN and Bi-LSTM
The electric energy consumption prediction (EECP) is an essential and complex task in
intelligent power management system. EECP plays a significant role in drawing up a …
intelligent power management system. EECP plays a significant role in drawing up a …
[HTML][HTML] Time series forecasting with multi-headed attention-based deep learning for residential energy consumption
Predicting residential energy consumption is tantamount to forecasting a multivariate time
series. A specific window for several sensor signals can induce various features extracted to …
series. A specific window for several sensor signals can induce various features extracted to …
[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 …
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 …
Prediction of residential building energy consumption: A neural network approach
Some of the challenges to predict energy utilization has gained recognition in the residential
sector due to the significant energy consumption in recent decades. However, the modeling …
sector due to the significant energy consumption in recent decades. However, the modeling …
A hybrid LSTM neural network for energy consumption forecasting of individual households
Irregular human behaviors and univariate datasets remain as two main obstacles of data-
driven energy consumption predictions for individual households. In this study, a hybrid …
driven energy consumption predictions for individual households. In this study, a hybrid …
A deep learning framework for building energy consumption forecast
Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …
impact, upsurges the need for the design of reliable energy demand forecast models. This …