Deep learning in smart grid technology: A review of recent advancements and future prospects

M Massaoudi, H Abu-Rub, SS Refaat, I Chihi… - IEEE …, 2021 - ieeexplore.ieee.org
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …

Roadmap on computational methods in optical imaging and holography

J Rosen, S Alford, B Allan, V Anand, S Arnon… - Applied Physics B, 2024 - Springer
Computational methods have been established as cornerstones in optical imaging and
holography in recent years. Every year, the dependence of optical imaging and holography …

Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand

C Sekhar, R Dahiya - Energy, 2023 - Elsevier
Buildings consume about half of the global electrical energy, and an accurate prediction of
their electricity consumption is crucial for building microgrids' efficient and reliable …

[HTML][HTML] District heater load forecasting based on machine learning and parallel CNN-LSTM attention

WH Chung, YH Gu, SJ Yoo - Energy, 2022 - Elsevier
Accurate heat load forecast is important to operate combined heat and power (CHP)
efficiently. This paper proposes a parallel convolutional neural network (CNN)-long short …

Time-series analysis with smoothed Convolutional Neural Network

AP Wibawa, ABP Utama, H Elmunsyah, U Pujianto… - Journal of big Data, 2022 - Springer
CNN originates from image processing and is not commonly known as a forecasting
technique in time-series analysis which depends on the quality of input data. One of the …

Machine learning for short-term load forecasting in smart grids

B Ibrahim, L Rabelo, E Gutierrez-Franco… - Energies, 2022 - mdpi.com
A smart grid is the future vision of power systems that will be enabled by artificial intelligence
(AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy …

An ensemble framework for short-term load forecasting based on parallel CNN and GRU with improved ResNet

H Hua, M Liu, Y Li, S Deng, Q Wang - Electric Power Systems Research, 2023 - Elsevier
Accurate and efficient load forecasting is of great significance for stable operation and
scheduling of modern power systems. However, load data are usually nonlinear and non …

CNN-LSTM vs. LSTM-CNN to predict power flow direction: a case study of the high-voltage subnet of northeast Germany

F Aksan, Y Li, V Suresh, P Janik - Sensors, 2023 - mdpi.com
The massive installation of renewable energy sources together with energy storage in the
power grid can lead to fluctuating energy consumption when there is a bi-directional power …

Advances in the application of machine learning techniques for power system analytics: A survey

SM Miraftabzadeh, M Longo, F Foiadelli, M Pasetti… - Energies, 2021 - mdpi.com
The recent advances in computing technologies and the increasing availability of large
amounts of data in smart grids and smart cities are generating new research opportunities in …

[HTML][HTML] Boosting energy harvesting via deep learning-based renewable power generation prediction

ZA Khan, T Hussain, SW Baik - Journal of King Saud University-Science, 2022 - Elsevier
The high-level variation of different energy generation resources makes the reliable power
supply significantly challenging to end-users. These variations occur due to the intermittent …