Electrical load forecasting models: A critical systematic review
C Kuster, Y Rezgui, M Mourshed - Sustainable cities and society, 2017 - Elsevier
Electricity forecasting is an essential component of smart grid, which has attracted
increasing academic interest. Forecasting enables informed and efficient responses for …
increasing academic interest. Forecasting enables informed and efficient responses for …
Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control
F Rodríguez, A Fleetwood, A Galarza, L Fontán - Renewable energy, 2018 - Elsevier
This paper proposes an artificial neural network (ANN) to predict the solar energy
generation produced by photovoltaic generators. The intermittent nature of solar power …
generation produced by photovoltaic generators. The intermittent nature of solar power …
An efficient deep learning framework for intelligent energy management in IoT networks
T Han, K Muhammad, T Hussain… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Green energy management is an economical solution for better energy usage, but the
employed literature lacks focusing on the potentials of edge intelligence in controllable …
employed literature lacks focusing on the potentials of edge intelligence in controllable …
Artificial intelligence and statistical techniques in short-term load forecasting: a review
Electrical utilities depend on short-term demand forecasting to proactively adjust production
and distribution in anticipation of major variations. This systematic review analyzes 240 …
and distribution in anticipation of major variations. This systematic review analyzes 240 …
基于人工智能技术的新型电力系统负荷预测研究综述
韩富佳， 王晓辉， 乔骥， 史梦洁， 蒲天骄 - 中国电机工程学报, 2023 - epjournal.csee.org.cn
在“双碳” 目标的驱动下, 构建以新能源为主体的新型电力系统是促进现代电力系统低碳转型发展
的重要前提与必然趋势. 由于复杂易变的多元负荷是新型电力系统的重要组成部分 …
的重要前提与必然趋势. 由于复杂易变的多元负荷是新型电力系统的重要组成部分 …
Artificial neural networks for short-term load forecasting in microgrids environment
The adaptation of energy production to demand has been traditionally very important for
utilities in order to optimize resource consumption. This is especially true also in microgrids …
utilities in order to optimize resource consumption. This is especially true also in microgrids …
Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks
O Laib, MT Khadir, L Mihaylova - Energy, 2019 - Elsevier
Finding suitable forecasting methods for an effective management of energy resources is of
paramount importance for improving the efficiency in energy consumption and decreasing …
paramount importance for improving the efficiency in energy consumption and decreasing …
A game theory-based energy management system using price elasticity for smart grids
Distributed devices in smart grid systems are decentralized and connected to the power grid
through different types of equipment transmit, which will produce numerous energy losses …
through different types of equipment transmit, which will produce numerous energy losses …
Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs
J Buitrago, S Asfour - Energies, 2017 - mdpi.com
Short-term load forecasting is crucial for the operations planning of an electrical grid.
Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize …
Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize …
[HTML][HTML] Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
management. Federated Learning (FL) is particularly noteworthy because it enhances data …