A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

S Aslam, H Herodotou, SM Mohsin, N Javaid… - … and Sustainable Energy …, 2021 - Elsevier
Microgrids have recently emerged as a building block for smart grids combining distributed
renewable energy sources (RESs), energy storage devices, and load management …

Applications of artificial intelligence and machine learning in smart cities

Z Ullah, F Al-Turjman, L Mostarda… - Computer Communications, 2020 - Elsevier
Smart cities are aimed to efficiently manage growing urbanization, energy consumption,
maintain a green environment, improve the economic and living standards of their citizens …

[HTML][HTML] Challenges, opportunities and future directions of smart manufacturing: a state of art review

S Phuyal, D Bista, R Bista - Sustainable Futures, 2020 - Elsevier
Smart manufacturing is the technology utilizing the interconnected machines and tools for
improving manufacturing performance and optimizing the energy and workforce required by …

Load forecasting techniques for power system: Research challenges and survey

N Ahmad, Y Ghadi, M Adnan, M Ali - IEEE Access, 2022 - ieeexplore.ieee.org
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …

A comprehensive review of the load forecasting techniques using single and hybrid predictive models

A Al Mamun, M Sohel, N Mohammad… - IEEE …, 2020 - ieeexplore.ieee.org
Load forecasting is a pivotal part of the power utility companies. To provide load-shedding
free and uninterrupted power to the consumer, decision-makers in the utility sector must …

A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India

S Chaturvedi, E Rajasekar, S Natarajan, N McCullen - Energy Policy, 2022 - Elsevier
Selecting a suitable energy demand forecasting method is challenging due to the complex
interplay of long-term trends, short-term seasonalities, and uncertainties. This paper …

Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning

M Tan, S Yuan, S Li, Y Su, H Li… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Power demand forecasting with high accuracy is a guarantee to keep the balance between
power supply and demand. Due to strong volatility of industrial power load, ultra-short-term …

Hydrogen fuel as an important element of the energy storage needs for future smart cities

Q Hassan, AZ Sameen, O Olapade, M Alghoul… - International Journal of …, 2023 - Elsevier
A considerable amount of non-dispatchable photovoltaic and wind power have always been
planned in smart cities, however, the problem of massive energy storage has not yet been …

IoT based smart and intelligent smart city energy optimization

Z Chen, CB Sivaparthipan, BA Muthu - Sustainable Energy Technologies …, 2022 - Elsevier
With the effective result of IoT architecture in all research areas, we propose IoT framework
based energy efficient smart and intelligent street road lighting system that consist of IoT …

[HTML][HTML] Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review

AE Onile, R Machlev, E Petlenkov, Y Levron, J Belikov - Energy Reports, 2021 - Elsevier
Innovative solutions targeting improvements in the behavior of energy consumers will be
required to achieve desired efficiency in the use of energy. Among other measures for …