Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption
Accurate energy consumption prediction models can bring tremendous benefits to building
energy efficiency, where the use of data-driven models allows models to be trained based …
energy efficiency, where the use of data-driven models allows models to be trained based …
A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting
L Fang, B He - Applied Energy, 2023 - Elsevier
Accurate energy load forecasting can not only provide favorable conditions for ensuring
energy security but also reduce carbon emissions and thereby slow down the process of …
energy security but also reduce carbon emissions and thereby slow down the process of …
[HTML][HTML] Enhancing interpretability in power management: A time-encoded household energy forecasting using hybrid deep learning model
Nowadays, residential households, including both consumers and emerging prosumers,
have exhibited a growing demand for active/reactive power. This demand surge arises from …
have exhibited a growing demand for active/reactive power. This demand surge arises from …
Artificial intelligence for enhanced flotation monitoring in the mining industry: A ConvLSTM-based approach
A Bendaouia, S Qassimi, A Boussetta… - Computers & Chemical …, 2024 - Elsevier
In the mining industry, accurate monitoring of the elemental composition in the flotation froth
is crucial for efficient minerals separation. The hybrid deep learning algorithms offer …
is crucial for efficient minerals separation. The hybrid deep learning algorithms offer …
A novel approach for breast cancer detection using optimized ensemble learning framework and XAI
Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading
contributor to cancer-related deaths in women worldwide. The timely identification of this …
contributor to cancer-related deaths in women worldwide. The timely identification of this …
Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network
Blood cancer has emerged as a growing concern over the past decade, necessitating early
diagnosis for timely and effective treatment. The present diagnostic method, which involves …
diagnosis for timely and effective treatment. The present diagnostic method, which involves …
Household energy consumption forecasting based on adaptive signal decomposition enhanced iTransformer network
With the presence of volatility and noise in the energy consumption data, existing energy
consumption forecasting methods have difficulties in achieving satisfactory forecasting …
consumption forecasting methods have difficulties in achieving satisfactory forecasting …
Wavelet CNN‐LSTM time series forecasting of electricity power generation considering biomass thermal systems
The use of biomass as a renewable energy source for electricity generation has gained
attention due to its sustainability and environmental benefits. However, the intermittent …
attention due to its sustainability and environmental benefits. However, the intermittent …
[HTML][HTML] A multivariate time series prediction method based on convolution-residual gated recurrent neural network and double-layer attention
C Cao, J Huang, M Wu, Z Lin, Y Sun - Electronics, 2024 - mdpi.com
In multivariate and multistep time series prediction research, we often face the problems of
insufficient spatial feature extraction and insufficient time-dependent mining of historical …
insufficient spatial feature extraction and insufficient time-dependent mining of historical …
Short‐term energy forecasting using deep neural networks: Prospects and challenges
S Tsegaye, P Sanjeevikumar… - The Journal of …, 2024 - Wiley Online Library
This study presents an in‐depth overview of deep neural networks (DNN) and their hybrid
applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from …
applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from …