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 …

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

V Nemani, L Biggio, X Huan, Z Hu, O Fink… - … Systems and Signal …, 2023 - Elsevier
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

[HTML][HTML] Advances of machine learning in multi-energy district communities‒mechanisms, applications and perspectives

Y Zhou - Energy and AI, 2022 - Elsevier
Energy paradigm transition towards the carbon neutrality requires combined and continuous
efforts in cleaner power production, advanced energy storages, flexible district energy …

[HTML][HTML] Artificial intelligence in renewable systems for transformation towards intelligent buildings

Y Zhou - Energy and AI, 2022 - Elsevier
Carbon-neutrality transition in building sectors requires combinations of renewable systems
and artificial intelligence (AI) for robustness, reliability, automation, and flexibility. In this …

An innovative optimal 4E solar-biomass waste polygeneration system for power, methanol, and freshwater production

SAM Rabeti, MHK Manesh, M Amidpour - Journal of Cleaner Production, 2023 - Elsevier
Modern polygeneration systems have increased the flexibility of production in energy
systems. In this paper, a polygeneration system for producing power, freshwater, and …

[HTML][HTML] GIScience can facilitate the development of solar cities for energy transition

R Zhu, MP Kwan, ATD Perera, H Fan, B Yang… - Advances in Applied …, 2023 - Elsevier
The energy transition is increasingly being discussed and implemented to cope with the
growing environmental crisis. However, great challenges remain for effectively harvesting …

An efficient data-driven optimal sizing framework for photovoltaics-battery-based electric vehicle charging microgrid

Y Wei, T Han, S Wang, Y Qin, L Lu, X Han… - Journal of Energy …, 2022 - Elsevier
The rapid growth of electric vehicles (EV) in cities has led to the development of microgrids
(MGs) combined with photovoltaics (PV) and the energy storage system (ESS) as charging …

Development of an ANN-based building energy model for information-poor buildings using transfer learning

A Li, F Xiao, C Fan, M Hu - Building simulation, 2021 - Springer
Accurate building energy prediction is vital to develop optimal control strategies to enhance
building energy efficiency and energy flexibility. In recent years, the data-driven approach …

Towards climate resilient urban energy systems: a review

VM Nik, ATD Perera, D Chen - National Science Review, 2021 - academic.oup.com
Climate change and increased urban population are two major concerns for society. Moving
towards more sustainable energy solutions in the urban context by integrating renewable …