Data mining and machine learning methods for sustainable smart cities traffic classification: A survey

M Shafiq, Z Tian, AK Bashir, A Jolfaei, X Yu - Sustainable Cities and …, 2020 - Elsevier
This survey paper describes the significant literature survey of Sustainable Smart Cities
(SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection …

Data mining in the construction industry: Present status, opportunities, and future trends

H Yan, N Yang, Y Peng, Y Ren - Automation in Construction, 2020 - Elsevier
The construction industry is experiencing remarkable growth in the data generation. Data
mining (DM) from considerable amount of data in the construction industry has emerged as …

CNN-LSTM architecture for predictive indoor temperature modeling

F Elmaz, R Eyckerman, W Casteels, S Latré… - Building and …, 2021 - Elsevier
Indoor temperature modeling is a crucial part towards efficient Heating, Ventilation and Air
Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive …

LSTM-autoencoder-based anomaly detection for indoor air quality time-series data

Y Wei, J Jang-Jaccard, W Xu, F Sabrina… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Anomaly detection for indoor air quality (IAQ) data has become an important area of
research as the quality of air is closely related to human health and well-being. However …

A spatial-temporal layer-wise relevance propagation method for improving interpretability and prediction accuracy of LSTM building energy prediction

G Li, F Li, C Xu, X Fang - Energy and Buildings, 2022 - Elsevier
At present, data-driven methods have achieved satisfactory results in building energy
consumption prediction, especially deep learning models such as long short-term memory …

Predictive control of HVAC by multiple output GRU-CFD integration approach to manage multiple IAQ for commercial heritage building preservation

J Zhang, KH Poon, HHL Kwok, F Hou… - Building and …, 2023 - Elsevier
Excessive and fluctuating indoor air quality (IAQ) leads to destruction of historical buildings.
Regenerated commercial historic buildings are generally fitted with heating, ventilation, and …

LSTM enhanced by dual-attention-based encoder-decoder for daily peak load forecasting

K Zhu, Y Li, W Mao, F Li, J Yan - Electric Power Systems Research, 2022 - Elsevier
Daily peak load forecasting is a challenging problem in the filed of electric power load
forecasting. Since the nonlinear and dynamic of influence factors and their sequential …

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 …

Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model

Z Fang, N Crimier, L Scanu, A Midelet, A Alyafi… - Energy and …, 2021 - Elsevier
Accurate indoor temperature forecasting can facilitate energy savings of the building without
compromising the occupant comfort level, by providing more accurate control of the HVAC …

A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

S Alawadi, D Mera, M Fernández-Delgado… - Energy Systems, 2020 - Springer
The international community has largely recognized that the Earth's climate is changing.
Mitigating its global effects requires international actions. The European Union (EU) is …