Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review

M Khalil, AS McGough, Z Pourmirza… - … Applications of Artificial …, 2022 - Elsevier
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 …

[HTML][HTML] Physical energy and data-driven models in building energy prediction: A review

Y Chen, M Guo, Z Chen, Z Chen, Y Ji - Energy Reports, 2022 - Elsevier
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 …

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 …

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 …

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 …

Residential building energy consumption estimation: a novel ensemble and hybrid machine learning approach

B Sadaghat, S Afzal, AJ Khiavi - Expert Systems with Applications, 2024 - Elsevier
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 …

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 …

Automated machine learning-based framework of heating and cooling load prediction for quick residential building design

C Lu, S Li, SR Penaka, T Olofsson - Energy, 2023 - Elsevier
Reducing the heating and cooling load through energy-efficient building design can help
decarbonize the building sector. Heating and cooling load prediction using machine …

[HTML][HTML] Building energy prediction models and related uncertainties: A review

J Yu, WS Chang, Y Dong - Buildings, 2022 - mdpi.com
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 …

Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review

Z Wang, L **a, H Yuan, RS Srinivasan… - Journal of Building …, 2022 - Elsevier
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 …