Load forecasting models in smart grid using smart meter information: a review

F Dewangan, AY Abdelaziz, M Biswal - Energies, 2023 - mdpi.com
The smart grid concept is introduced to accelerate the operational efficiency and enhance
the reliability and sustainability of power supply by operating in self-control mode to find and …

[HTML][HTML] Smart home energy management systems: Research challenges and survey

A Raza, L **gzhao, Y Ghadi, M Adnan, M Ali - Alexandria engineering …, 2024 - Elsevier
Electricity is establishing ground as a means of energy, and its proportion will continue to
rise in the next generations. Home energy usage is expected to increase by more than 40 …

Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation

M Ghiasi, Z Wang, M Mehrandezh, S Jalilian… - IET Smart …, 2023 - Wiley Online Library
To achieve low‐carbon sustainable energy development, new technologies such as Internet
of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy …

[HTML][HTML] An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage …

R Sepehrzad, ASG Langeroudi, A Khodadadi… - Sustainable Cities and …, 2024 - Elsevier
This study proposed an intelligent energy management strategy for islanded networked
microgrids (NMGs) in smart cities considering the renewable energy sources uncertainties …

[HTML][HTML] Driving support by type-2 fuzzy logic control model

M Woźniak, A Zielonka, A Sikora - Expert Systems with Applications, 2022 - Elsevier
Abstract Advanced models of Artificial Intelligence enable systems of IoT to work with great
flexibility to the needs of users. In this article we present our developed IoT system for driving …

[HTML][HTML] Optimal load forecasting and scheduling strategies for smart homes peer-to-peer energy networks: A comprehensive survey with critical simulation analysis

A Raza, L **gzhao, M Adnan, I Ahmad - Results in Engineering, 2024 - Elsevier
The home energy management (HEM) sector is going through an enormous change that
includes important elements like incorporating green power, enhancing efficiency through …

[HTML][HTML] Enhancing electrical load prediction using a bidirectional LSTM neural network

C Pavlatos, E Makris, G Fotis, V Vita, V Mladenov - Electronics, 2023 - mdpi.com
Precise anticipation of electrical demand holds crucial importance for the optimal operation
of power systems and the effective management of energy markets within the domain of …

A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems

M Ali, G Kaddoum, WT Li, C Yuen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable
vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing …

Memory long and short term time series network for ultra-short-term photovoltaic power forecasting

C Huang, M Yang - Energy, 2023 - Elsevier
Photovoltaic (PV) power is stochastic, intermittent and volatile, which has brought huge
challenges to the safe and stable operation of the power grid. Accurate PV power …

Random vector functional link neural network based ensemble deep learning for short-term load forecasting

R Gao, L Du, PN Suganthan, Q Zhou… - Expert Systems with …, 2022 - Elsevier
Electric load forecasting is essential for the planning and maintenance of power systems.
However, its un-stationary and non-linear properties impose significant difficulties in …