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(PDF) Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings
ResearchGate
FIGURE 1 Flow chart of the proposed methodology. PANDIT ET AL. |. 3. 3|METHODOLOGIES AND. EXPERIMENTAL SETUP. 3.1 |LSTM theoretical descriptions.
1 month ago
Short-term air quality prediction based on EMD-transformer-BiLSTM
Nature
This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory...
2 months ago
Enhancing Load Forecasting Accuracy in Smart Grids: A Novel Parallel Multichannel Network Approach Using 1D CNN and Bi-LSTM Models
Wiley Online Library
This study considers a novel approach to independently process features for spatial and temporal characteristics using a parallel multichannel network.
3 months ago
(PDF) Predictive Analytics for Demand Forecasting: A deep Learning-based Decision Support System
ResearchGate
This review paper explores the transformative role of predictive analytics and deep learning in demand forecasting.
3 months ago
Deep learning-driven hybrid model for short-term load forecasting and smart grid information management
Nature
Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the...
4 months ago
ESG guidance and artificial intelligence support for power systems analytics in the energy industry
Nature
This study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG).
5 months ago
Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings
Wiley Online Library
The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and...
5 months ago
A novel deep-learning framework for short-term prediction of cooling load in public buildings
ScienceDirect.com
A novel deep learning-based prediction framework, aTCN-LSTM, is proposed. First, a gate-controlled multi-head temporal convolutional network is designed.
10 months ago
A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm
ResearchGate
Abstract—A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by.
10 months ago
Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network
Nature
This article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the...
11 months ago