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LSTM based long-term energy consumption prediction with periodicity
JQ Wang, Y Du, J Wang - energy, 2020 - Elsevier
Energy consumption information is a kind of time series with periodicity in many real system,
while the general forecasting methods do not concern periodicity. This paper proposes a …
while the general forecasting methods do not concern periodicity. This paper proposes a …
Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
In this paper, an efficient new hybrid time series forecasting model combining variational
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
Bridge deformation prediction based on SHM data using improved VMD and conditional KDE
J ** countries. During the last decade several new techniques are being used for …
Ozone concentration forecasting based on artificial intelligence techniques: a systematic review
The prediction of tropospheric ozone concentrations is vital due to ozone's passive impacts
on atmosphere, people's health, flora and fauna. However, ozone prediction is a complex …
on atmosphere, people's health, flora and fauna. However, ozone prediction is a complex …
[HTML][HTML] A deep learning multi-layer perceptron and remote sensing approach for soil health based crop yield estimation
Abstract In recent years, Deep Learning Multi-Layer Perceptron (DLMLP) neural networks
have shown remarkable success in addressing crop yield forecast related problems. The …
have shown remarkable success in addressing crop yield forecast related problems. The …
Application and performance of data mining techniques in stock market: A review
Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional
forecasting methods and the pursuit to uncover the hidden patterns in stock market data …
forecasting methods and the pursuit to uncover the hidden patterns in stock market data …
A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality …
Y Zhang, J Le, X Liao, F Zheng, Y Li - Energy, 2019 - Elsevier
Accurate wind power prediction can alleviate the negative influence on power system
caused by the integration of wind farms into grid. In this paper, a novel combination model is …
caused by the integration of wind farms into grid. In this paper, a novel combination model is …
[HTML][HTML] Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels
A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search
algorithm (AM-SSA), called AMSSA-Elman-AdaBoost, is proposed for predicting the existing …
algorithm (AM-SSA), called AMSSA-Elman-AdaBoost, is proposed for predicting the existing …
[HTML][HTML] An improved gradient boosting regression tree estimation model for soil heavy metal (Arsenic) pollution monitoring using hyperspectral remote sensing
Hyperspectral remote sensing can be used to effectively identify contaminated elements in
soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote …
soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote …