Exploring the challenges and opportunities using big data in the smart grid to increase efficiency. Presenting big data, life-cycle, and its place in the smart grid and applications. From: Sustainable Networks in Smart Grid, 2022. Providing a classification of machine learning and deep learning methods by type of learning used in the smart grid. Smart power grid station: IoT is associating the actual world with the virtual universe of the Internet. Reviewing conventional methods for analyzing data in the power grid compared with data-driven technics and expressing the advantages and disadvantages of each category. It will give grid operators new tools to reduce power demand quickly when wind or solar power dips, and it will have more energy storage capabilities to absorb excess wind and solar power when it isn't needed, then to release that energy when the wind and solar power dips. ![]() Presenting artificial intelligence and a variety of machine learning and deep learning methods used in the smart grid. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. The Smart Grid will be able to make better use of these energy resources. Also, the role of big data in smart power grids, and its features such life cycle, and efficient services such as forecast, predictive maintenance, and fault detection are discussed. Power moves down that chain from generators (such as turbines and solar arrays) to very high-voltage transmission lines, to. In this paper, the state-of-the-art approaches based on artificial intelligence used by smart power grids for applications and data sources are investigated. The grid is a chain of interconnected machines spanning North America. These methods are able to process huge amounts of data and propose an appropriate solution to solve power industry complex problems. The project draws from global experience and. To this end, machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) can be applied. Smart Grids (ISGAN TCP) The IEA is expanding cross-Agency efforts to assess the policy, regulatory, technology and investment context needed to accelerate progress on power system modernisation and effective utilisation of demand side resources, leveraging the opportunities offered by digitalisation. Artificial intelligence methods can offer data-driven services by extracting valuable information which is produced by meter devices and sensors in smart grids. This process is changing continuously and needs advanced methods to process big data produced by different segments. ![]() Faculty of Computer Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iranīig Data Research Center- Najafabad Branch, Islamic Azad University, Najafabad, IranĪs a promising vision toward obtaining high reliability and better energy management, nowadays power grid is transferring to the smart grid (SG).
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