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Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review
The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …
[HTML][HTML] Physical energy and data-driven models in building energy prediction: A review
The difficulty in balancing energy supply and demand is increasing due to the growth of
diversified and flexible building energy resources, particularly the rapid development of …
diversified and flexible building energy resources, particularly the rapid development of …
Building energy prediction using artificial neural networks: A literature survey
C Lu, S Li, Z Lu - Energy and Buildings, 2022 - Elsevier
Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …
improving energy efficiency in building energy management systems. Essentially, building …
Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection
W Huo, W Li, Z Zhang, C Sun, F Zhou… - Energy Conversion and …, 2021 - Elsevier
For optimizing the performance of the proton exchange membrane fuel cells (PEMFCs), the I–
V polarization curve is generally used as an important evaluation metric, which can …
V polarization curve is generally used as an important evaluation metric, which can …
Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm
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 …
Residential building energy consumption estimation: a novel ensemble and hybrid machine learning approach
In recent decades, there has been a substantial rise in both worldwide energy consumption
and the accompanying increase in Carbon Dioxide (CO 2) emissions, primarily propelled by …
and the accompanying increase in Carbon Dioxide (CO 2) emissions, primarily propelled by …
Attention-LSTM architecture combined with Bayesian hyperparameter optimization for indoor temperature prediction
B Jiang, H Gong, H Qin, M Zhu - Building and Environment, 2022 - Elsevier
Accurate prediction of indoor temperature can provide more reference data for indoor
thermal comfort assessment and the operational effectiveness of heating, ventilation and air …
thermal comfort assessment and the operational effectiveness of heating, ventilation and air …
Automated machine learning-based framework of heating and cooling load prediction for quick residential building design
Reducing the heating and cooling load through energy-efficient building design can help
decarbonize the building sector. Heating and cooling load prediction using machine …
decarbonize the building sector. Heating and cooling load prediction using machine …
[HTML][HTML] Building energy prediction models and related uncertainties: A review
Building energy usage has been an important issue in recent decades, and energy
prediction models are important tools for analysing this problem. This study provides a …
prediction models are important tools for analysing this problem. This study provides a …
Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review
With the rapid growth in the volume of relevant and available data, feature engineering is
emerging as a popular research subject in data-driven building energy prediction owing to …
emerging as a popular research subject in data-driven building energy prediction owing to …