[HTML][HTML] Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
Recent years have seen an increasing interest in Demand Response (DR) as a means to
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …
IoT-based enterprise resource planning: Challenges, open issues, applications, architecture, and future research directions
In today's highly competitive markets, organizations can create a competitive advantage
through the successful implementation of Enterprise Resource Planning (ERP) systems …
through the successful implementation of Enterprise Resource Planning (ERP) systems …
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 …
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
R Lu, SH Hong - Applied energy, 2019 - Elsevier
Balancing electricity generation and consumption is essential for smoothing the power grids.
Any mismatch between energy supply and demand would increase costs to both the service …
Any mismatch between energy supply and demand would increase costs to both the service …
Demand response for home energy management using reinforcement learning and artificial neural network
Ever-changing variables in the electricity market require energy management systems
(EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest …
(EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest …
A dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach
R Lu, SH Hong, X Zhang - Applied energy, 2018 - Elsevier
With the modern advanced information and communication technologies in smart grid
systems, demand response (DR) has become an effective method for improving grid …
systems, demand response (DR) has become an effective method for improving grid …
Data management in industry 4.0: State of the art and open challenges
Information and communication technologies are permeating all aspects of industrial and
manufacturing systems, expediting the generation of large volumes of industrial data. This …
manufacturing systems, expediting the generation of large volumes of industrial data. This …
Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences
The increasing penetration of renewable energy in power grids introduces higher levels of
uncertainty, while current decision-making models typically favour either a risk-averse or risk …
uncertainty, while current decision-making models typically favour either a risk-averse or risk …
[HTML][HTML] A combined deep learning application for short term load forecasting
An accurate prediction of buildings' load demand is one of the most important issues in
smart grid and smart building applications. In this way, an important contribution is made to …
smart grid and smart building applications. In this way, an important contribution is made to …
Data-driven real-time price-based demand response for industrial facilities energy management
Recent advances in smart grid technologies have highlighted demand response (DR) as an
important tool to alleviate electricity demand–supply mismatches. In this paper, a real-time …
important tool to alleviate electricity demand–supply mismatches. In this paper, a real-time …