A review of machine learning in building load prediction

L Zhang, J Wen, Y Li, J Chen, Y Ye, Y Fu, W Livingood - Applied Energy, 2021 - Elsevier
The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …

An experimental review on deep learning architectures for time series forecasting

P Lara-Benítez, M Carranza-García… - International journal of …, 2021 - World Scientific
In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

A BIM-data mining integrated digital twin framework for advanced project management

Y Pan, L Zhang - Automation in Construction, 2021 - Elsevier
With the focus of smart construction project management, this paper presents a closed-loop
digital twin framework under the integration of Building Information Modeling (BIM), Internet …

Long sequence time-series forecasting with deep learning: A survey

Z Chen, M Ma, T Li, H Wang, C Li - Information Fusion, 2023 - Elsevier
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

[HTML][HTML] Forecasting: theory and practice

F Petropoulos, D Apiletti, V Assimakopoulos… - International Journal of …, 2022 - Elsevier
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

A deep learning framework for building energy consumption forecast

N Somu, GR MR, K Ramamritham - Renewable and Sustainable Energy …, 2021 - Elsevier
Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …

Machine learning and deep learning in energy systems: A review

MM Forootan, I Larki, R Zahedi, A Ahmadi - Sustainability, 2022 - mdpi.com
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …

[HTML][HTML] Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

Z Liu, Y Sun, C Xing, J Liu, Y He, Y Zhou, G Zhang - Energy and AI, 2022 - Elsevier
The vigorous expansion of renewable energy as a substitute for fossil energy is the
predominant route of action to achieve worldwide carbon neutrality. However, clean energy …

A novel CNN-GRU-based hybrid approach for short-term residential load forecasting

M Sajjad, ZA Khan, A Ullah, T Hussain, W Ullah… - Ieee …, 2020 - ieeexplore.ieee.org
Electric energy forecasting domain attracts researchers due to its key role in saving energy
resources, where mainstream existing models are based on Gradient Boosting Regression …

Predicting residential energy consumption using CNN-LSTM neural networks

TY Kim, SB Cho - Energy, 2019 - Elsevier
The rapid increase in human population and development in technology have sharply
raised power consumption in today's world. Since electricity is consumed simultaneously as …