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 water quality variable prediction using a hybrid CNN–LSTM deep learning model
Water quality monitoring is an important component of water resources management. In
order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and …
order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and …
[HTML][HTML] Electricity theft detection in smart grid systems: A CNN-LSTM based approach
Among an electricity provider's non-technical losses, electricity theft has the most severe and
dangerous effects. Fraudulent electricity consumption decreases the supply quality …
dangerous effects. Fraudulent electricity consumption decreases the supply quality …
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market …
W Li, DM Becker - Energy, 2021 - Elsevier
The availability of accurate day-ahead electricity price forecasts is pivotal for electricity
market participants. In the context of trade liberalisation and market harmonisation in the …
market participants. In the context of trade liberalisation and market harmonisation in the …
[HTML][HTML] Electric energy consumption prediction by deep learning with state explainable autoencoder
As energy demand grows globally, the energy management system (EMS) is becoming
increasingly important. Energy prediction is an essential component in the first step to create …
increasingly important. Energy prediction is an essential component in the first step to create …
Hybrid convolutional neural network-multilayer perceptron model for solar radiation prediction
Urgent transition from the dependence on fossil fuels towards renewable energies requires
more solar photovoltaic power to be connected to the electricity grids, with reliable supply …
more solar photovoltaic power to be connected to the electricity grids, with reliable supply …
[HTML][HTML] Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction
DG da Silva, AA de Moura Meneses - Energy Reports, 2023 - Elsevier
Electric consumption prediction methods are investigated for many reasons, such as
decision-making related to energy efficiency as well as for anticipating demand and the …
decision-making related to energy efficiency as well as for anticipating demand and the …
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 …
[HTML][HTML] Long-term power load forecasting using LSTM-informer with ensemble learning
K Wang, J Zhang, X Li, Y Zhang - Electronics, 2023 - mdpi.com
Accurate power load forecasting can facilitate effective distribution of power and avoid
wasting power so as to reduce costs. Power load is affected by many factors, so accurate …
wasting power so as to reduce costs. Power load is affected by many factors, so accurate …
A hybrid convolutional recurrent (CNN-GRU) model for stock price prediction
Stock price forecasting systems are on-demand that used for prediction in the financial
world. The deep learning models are used for handling large data and making predictions …
world. The deep learning models are used for handling large data and making predictions …