Integrating renewable sources into energy system for smart city as a sagacious strategy towards clean and sustainable process
AT Hoang, XP Nguyen - Journal of Cleaner Production, 2021 - Elsevier
Among the main components of a smart city, the energy system plays a vital and core role in
the transition towards a sustainable urban life. Furthermore, the utilization of renewable …
the transition towards a sustainable urban life. Furthermore, the utilization of renewable …
A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Microgrids have recently emerged as a building block for smart grids combining distributed
renewable energy sources (RESs), energy storage devices, and load management …
renewable energy sources (RESs), energy storage devices, and load management …
[HTML][HTML] A survey of explainable artificial intelligence for smart cities
The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans
and envisioned the concept of smart cities using informed actions, enhanced user …
and envisioned the concept of smart cities using informed actions, enhanced user …
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting
This paper proposes an effective computing framework for Short-Term Load Forecasting
(STLF). The proposed technique copes with the stochastic variations of the load demand …
(STLF). The proposed technique copes with the stochastic variations of the load demand …
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
[HTML][HTML] Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches
Background: With the development of smart grids, accurate electric load forecasting has
become increasingly important as it can help power companies in better load scheduling …
become increasingly important as it can help power companies in better load scheduling …
[HTML][HTML] Challenges, opportunities and future directions of smart manufacturing: a state of art review
Smart manufacturing is the technology utilizing the interconnected machines and tools for
improving manufacturing performance and optimizing the energy and workforce required by …
improving manufacturing performance and optimizing the energy and workforce required by …
Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid
G Hafeez, KS Alimgeer, I Khan - Applied Energy, 2020 - Elsevier
Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …
and operation of the power grid. However, the electric load profile is a complex signal due to …
Building energy load forecasting using deep neural networks
DL Marino, K Amarasinghe… - IECON 2016-42nd annual …, 2016 - ieeexplore.ieee.org
Ensuring sustainability demands more efficient energy management with minimized energy
wastage. Therefore, the power grid of the future should provide an unprecedented level of …
wastage. Therefore, the power grid of the future should provide an unprecedented level of …
Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian …
M Sharifzadeh, A Sikinioti-Lock, N Shah - Renewable and Sustainable …, 2019 - Elsevier
Renewable energy from wind and solar resources can contribute significantly to the
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …