[HTML][HTML] Energetics Systems and artificial intelligence: Applications of industry 4.0
Industrial development with the growth, strengthening, stability, technical advancement,
reliability, selection, and dynamic response of the power system is essential. Governments …
reliability, selection, and dynamic response of the power system is essential. Governments …
Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques
PJ García-Nieto, E Garcia-Gonzalo… - Neural Computing and …, 2021 - Springer
This study builds a predictive model capable of estimating the critical temperature of a
superconductor from experimentally determined physico-chemical properties of the material …
superconductor from experimentally determined physico-chemical properties of the material …
Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature
Short-term forecasting of building energy consumption (BEC) is significant for building
energy reduction and real-time demand response. In this study, we propose a new method …
energy reduction and real-time demand response. In this study, we propose a new method …
[HTML][HTML] Improving water quality index prediction using regression learning models
Rivers are the main sources of freshwater supply for the world population. However, many
economic activities contribute to river water pollution. River water quality can be monitored …
economic activities contribute to river water pollution. River water quality can be monitored …
Efficient data-driven models for prediction and optimization of geothermal power plant operations
Increasing the capacity of geothermal energy as a renewable resource calls for
development and deployment of efficient control and optimization technologies for …
development and deployment of efficient control and optimization technologies for …
Modified sparse regression to solve heterogeneity and hybrid models for increasing the prediction accuracy of seaweed big data with outliers
The linear regression is critical for data modelling, especially for scientists. Nevertheless,
with the plenty of high-dimensional data, there are data with more explanatory variables …
with the plenty of high-dimensional data, there are data with more explanatory variables …
Deep learning with long short-term memory networks for air temperature predictions
C Li, Y Zhang, G Zhao - 2019 International conference on …, 2019 - ieeexplore.ieee.org
Temperature is a commonly used meteorological variable that plays an important role in
society, agricultural production and the economy. In this paper, a stacked long short-term …
society, agricultural production and the economy. In this paper, a stacked long short-term …
A refinement of lasso regression applied to temperature forecasting
Abstract Model predictive controllers use accurate temperature forecasts to save energy by
optimally controlling heating, ventilation and air conditioning equipment while achieving …
optimally controlling heating, ventilation and air conditioning equipment while achieving …
[HTML][HTML] Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting
R Liu, W He, H Dong, T Han, Y Yang, H Yu, Z Li - Energies, 2024 - mdpi.com
As global climate change intensifies, nations around the world are implementing policies
aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market …
aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market …
Comparative study on the correlation between human local and overall thermal sensations based on supervised machine learning
H Zhao, B **a, J Zhao, S Zhao, H Kuai, X Zhang… - Energy and …, 2025 - Elsevier
In heterogeneous indoor environments, significant perceptual discrepancies exist among
different body parts concerning their environmental sensitivity. Understanding the …
different body parts concerning their environmental sensitivity. Understanding the …