State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques

R Wazirali, E Yaghoubi, MSS Abujazar… - Electric power systems …, 2023 - Elsevier
Forecasting renewable energy efficiency significantly impacts system management and
operation because more precise forecasts mean reduced risk and improved stability and …

[HTML][HTML] A hybrid stacking model for enhanced short-term load forecasting

F Guo, H Mo, J Wu, L Pan, H Zhou, Z Zhang, L Li… - Electronics, 2024 - mdpi.com
The high penetration of distributed energy resources poses significant challenges to the
dispatch and operation of power systems. Improving the accuracy of short-term load …

[HTML][HTML] A critical analysis of different power quality improvement techniques in microgrid

S Choudhury, GK Sahoo - e-Prime-Advances in Electrical Engineering …, 2024 - Elsevier
Recently, the exponential decay of traditional petroleum and coal-based reserves with the
ever-rising energy demand has led to the need for alternate energy sources. Distributed …

[HTML][HTML] A novel cyber-Resilient solar power forecasting model based on secure federated deep learning and data visualization

A Moradzadeh, H Moayyed, B Mohammadi-Ivatloo… - Renewable Energy, 2023 - Elsevier
Improving the accuracy of photovoltaic (PV) power forecasting is crucial to ensure more
effective use of energy resources. Improvements are especially important for regions for …

[HTML][HTML] Smart grid enterprise decision-making and economic benefit analysis based on LSTM-GAN and edge computing algorithm

P Yang, S Li, S Qin, L Wang, M Hu, F Yang - Alexandria Engineering …, 2024 - Elsevier
As the next-generation power system, smart grid presents challenges to enterprises in
managing and analyzing massive data, meeting complex operational and decision-making …

Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management

W Waheed, Q Xu - Electric Power Systems Research, 2024 - Elsevier
In today's smart grid and building infrastructure, it is strongly suggested to implement short-
term demand forecasting for future power generation. There is a growing demand for …

probabilistic forecasting of residential energy consumption based on SWT-QRTCN-ADSC-NLSTM Model

N Jin, L Song, GJ Huang, K Yan - Information, 2023 - mdpi.com
Residential electricity consumption forecasting plays a crucial role in the rational allocation
of resources reducing energy waste and enhancing the grid-connected operation of power …

Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

C Zheng, T Peng, Z Chao, Z Shasha… - Mobile Information …, 2022 - Wiley Online Library
Electric buses have a significant penetration rate and high charging frequency and amount.
Therefore, their charging load has a momentous influence on the power grid's operation and …

Federated Deep Learning Technique for Power and Energy Systems Data Analysis

H Moayyed, A Moradzadeh… - … Data Mining and …, 2022 - Wiley Online Library
In recent years, there has been a tremendous increase in the application of information and
communication technologies (ICTs) in critical infrastructures, such as energy grids and …

[PDF][PDF] e-Prime-Advances in Electrical Engineering, Electronics and Energy

S Choudhury, GK Sahoo - researchgate.net
Recently, the exponential decay of traditional petroleum and coal-based reserves with the
ever-rising energy demand has led to the need for alternate energy sources. Distributed …