Systematic review of deep learning and machine learning for building energy
S Ardabili, L Abdolalizadeh, C Mako, B Torok… - Frontiers in Energy …, 2022 - frontiersin.org
The building energy (BE) management plays an essential role in urban sustainability and
smart cities. Recently, the novel data science and data-driven technologies have shown …
smart cities. Recently, the novel data science and data-driven technologies have shown …
Natural gas consumption forecasting: A discussion on forecasting history and future challenges
J Liu, S Wang, N Wei, X Chen, H Xie, J Wang - Journal of Natural Gas …, 2021 - Elsevier
Natural gas consumption forecasting technology has been researched for 70 years. This
paper reviews the history of natural gas consumption forecasting, and discusses the …
paper reviews the history of natural gas consumption forecasting, and discusses the …
Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting
P Kumari, D Toshniwal - Applied Energy, 2021 - Elsevier
The volatile behavior of solar energy is the biggest challenge in its successful integration
with existing grid systems. Accurate global horizontal irradiance (GHI) forecasting can …
with existing grid systems. Accurate global horizontal irradiance (GHI) forecasting can …
Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm
XJ Luo, LO Oyedele - Advanced Engineering Informatics, 2021 - Elsevier
The real-world building can be regarded as a comprehensive energy engineering system;
its actual energy consumption depends on complex affecting factors, including various …
its actual energy consumption depends on complex affecting factors, including various …
Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM
H Zeng, B Shao, H Dai, Y Yan, N Tian - Energy, 2023 - Elsevier
An accurate prediction on natural gas load is always a guarantee of a safe and reliable
operation of the natural gas pipeline network system, however, natural gas daily load …
operation of the natural gas pipeline network system, however, natural gas daily load …
Load forecasting with machine learning and deep learning methods
M Cordeiro-Costas, D Villanueva, P Eguía-Oller… - Applied Sciences, 2023 - mdpi.com
Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …
buildings without any structural change and is the basis for controlling and optimizing …
[HTML][HTML] Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks
Public charging station occupancy prediction plays key importance in developing a smart
charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However …
charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However …
Daily natural gas load forecasting based on the combination of long short term memory, local mean decomposition, and wavelet threshold denoising algorithm
S Peng, R Chen, B Yu, M Xiang, X Lin, E Liu - Journal of Natural Gas …, 2021 - Elsevier
With the opening of the third energy revolution, the booming development of the natural gas
industry in China has led to the existing pipeline network facilities no longer meeting the gas …
industry in China has led to the existing pipeline network facilities no longer meeting the gas …
Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms
ME Javanmard, SF Ghaderi - Sustainable Cities and Society, 2023 - Elsevier
With the growth of population, many countries face the challenge of supplying energy
resources. One approach to managing and planning these resources is to predict energy …
resources. One approach to managing and planning these resources is to predict energy …
TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets
ALA Dalal, AM AlRassas, MAA Al-qaness, Z Cai… - Applied Energy, 2023 - Elsevier
Due to weather and political fluctuations that significantly impact the production and price of
energy sources, enhancing data distribution and reducing data complexity is crucial to …
energy sources, enhancing data distribution and reducing data complexity is crucial to …