[HTML][HTML] Implementation of artificial intelligence techniques in microgrid control environment: Current progress and future scopes

R Trivedi, S Khadem - Energy and AI, 2022 - Elsevier
Microgrids are gaining popularity by facilitating distributed energy resources (DERs) and
forming essential consumer/prosumer centric integrated energy systems. Integration …

[HTML][HTML] An insight of deep learning based demand forecasting in smart grids

JM Aguiar-Pérez, MÁ Pérez-Juárez - Sensors, 2023 - mdpi.com
Smart grids are able to forecast customers' consumption patterns, ie, their energy demand,
and consequently electricity can be transmitted after taking into account the expected …

Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks

MN Fekri, K Grolinger, S Mir - International Journal of Electrical Power & …, 2022 - Elsevier
Load forecasting is essential for energy management, infrastructure planning, grid
operation, and budgeting. Large scale smart meter deployments have resulted in ability to …

Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm

XJ Luo, LO Oyedele - Advanced Engineering Informatics, 2021 - Elsevier
The real-world building can be regarded as a comprehensive energy engineering system;
its actual energy consumption depends on complex affecting factors, including various …

[HTML][HTML] Green hydrogen production potential in West Africa–Case of Niger

R Bhandari - Renewable Energy, 2022 - Elsevier
Niger offers the possibility of producing green hydrogen due to its high solar energy
potential. Due to the still growing domestic oil and coal industry, the use of green hydrogen …

[HTML][HTML] Forecasting of electric load using a hybrid LSTM-neural prophet model

MJA Shohan, MO Faruque, SY Foo - Energies, 2022 - mdpi.com
Load forecasting (LF) is an essential factor in power system management. LF helps the utility
maximize the utilization of power-generating plants and schedule them both reliably and …

[HTML][HTML] Privacy-preserving federated learning for residential short-term load forecasting

JD Fernández, SP Menci, CM Lee, A Rieger, G Fridgen - Applied energy, 2022 - Elsevier
With high levels of intermittent power generation and dynamic demand patterns, accurate
forecasts for residential loads have become essential. Smart meters can play an important …

Techno-economic feasibility analysis of an electric vehicle charging station for an International Airport in Chattogram, Bangladesh

S Hasan, M Zeyad, SMM Ahmed, DM Mahmud… - Energy Conversion and …, 2023 - Elsevier
The transportation system is one of the crucial requirements of day-to-day human life. The
car is one of the most attractive modes of transportation system for human beings. The …

Efficient residential electric load forecasting via transfer learning and graph neural networks

D Wu, W Lin - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The accurate short-term electric load forecasting (STLF) is critical for the safety and
economical operation of modern electric power systems. Recently, the graph neural network …

[HTML][HTML] Short-term electricity load forecasting—A systematic approach from system level to secondary substations

MG Pinheiro, SC Madeira, AP Francisco - Applied Energy, 2023 - Elsevier
Energy forecasting covers a wide range of prediction problems in the utility industry, such as
forecasting demand, generation, price, and power load over time horizons and different …