Machine learning for estimation of building energy consumption and performance: a review

S Seyedzadeh, FP Rahimian, I Glesk… - Visualization in …, 2018 - Springer
Ever growing population and progressive municipal business demands for constructing new
buildings are known as the foremost contributor to greenhouse gasses. Therefore …

Machine learning-enabled analysis of product distribution and composition in biomass-coal co-pyrolysis

A Shafizadeh, H Shahbeik, S Rafiee, Z Fardi, K Karimi… - Fuel, 2024 - Elsevier
Co-pyrolysis of biomass and coal presents a promising opportunity for large-scale biomass
utilization while reducing fossil fuel consumption. However, this process is highly complex …

Deep learning framework to forecast electricity demand

J Bedi, D Toshniwal - Applied energy, 2019 - Elsevier
The increasing world population and availability of energy hungry smart devices are major
reasons for alarmingly high electricity consumption in the current times. So far, various …

Building energy load forecasting using deep neural networks

DL Marino, K Amarasinghe… - IECON 2016-42nd annual …, 2016 - ieeexplore.ieee.org
Ensuring sustainability demands more efficient energy management with minimized energy
wastage. Therefore, the power grid of the future should provide an unprecedented level of …

Modeling techniques used in building HVAC control systems: A review

Z Afroz, GM Shafiullah, T Urmee, G Higgins - Renewable and sustainable …, 2018 - Elsevier
The appropriate application of advanced control strategies in Heating, Ventilation, and Air-
conditioning (HVAC) systems is key to improving the energy efficiency of buildings …

Deep neural networks for energy load forecasting

K Amarasinghe, DL Marino… - 2017 IEEE 26th …, 2017 - ieeexplore.ieee.org
Smartgrids of the future promise unprecedented flexibility in energy management. Therefore,
accurate predictions/forecasts of energy demands (loads) at individual site and aggregate …

Predicting the energy consumption in buildings using the optimized support vector regression model

W Cai, X Wen, C Li, J Shao, J Xu - Energy, 2023 - Elsevier
One of the most significant axes of regional, national, and worldwide energy policy is energy
efficiency in building design. In particular, the energy efficiency of HVAC systems is of …

Modelling of a multi-stage energy management control routine for energy demand forecasting, flexibility, and optimization of smart communities using a Recurrent …

A Petrucci, G Barone, A Buonomano… - Energy Conversion and …, 2022 - Elsevier
This paper proposes an innovative algorithm for community energy management control,
able to involve customers in energy trading by exploiting their potential energy flexibility. The …

Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings

Y Chen, Z Tong, Y Zheng, H Samuelson… - Journal of Cleaner …, 2020 - Elsevier
Advanced control strategies are central components of smart buildings. For model-based
control algorithms, the quality of the model that represents building systems and dynamics is …

A comprehensive review on the application of artificial neural networks in building energy analysis

SR Mohandes, X Zhang, A Mahdiyar - Neurocomputing, 2019 - Elsevier
This paper presents a comprehensive review of the significant studies exploited Artificial
Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of …