Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …

T Ahmad, R Madonski, D Zhang, C Huang… - … and Sustainable Energy …, 2022 - Elsevier
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …

[HTML][HTML] Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

W Khan, S Walker, W Zeiler - Energy, 2022 - Elsevier
An accurate solar energy forecast is of utmost importance to allow a higher level of
integration of renewable energy into the controls of the existing electricity grid. With the …

[HTML][HTML] Energetics Systems and artificial intelligence: Applications of industry 4.0

T Ahmad, H Zhu, D Zhang, R Tariq, A Bassam, F Ullah… - Energy Reports, 2022 - Elsevier
Industrial development with the growth, strengthening, stability, technical advancement,
reliability, selection, and dynamic response of the power system is essential. Governments …

[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 **ng, 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 …

Overview of smart grid implementation: Frameworks, impact, performance and challenges

MA Judge, A Khan, A Manzoor, HA Khattak - Journal of Energy Storage, 2022 - Elsevier
High consumption and ever-increasing demand for electricity at commercial, residential, and
industrial levels have attracted the research community to look for new technologies for the …

Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

MN Fekri, H Patel, K Grolinger, V Sharma - Applied Energy, 2021 - Elsevier
Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …