Comparing computers from 2024 to 2014, we can notice a difference. We are jumping from 4 GB of RAM to 32 GB. The microprocessors are also performed. while most people use a dual-core or I3 intel processor of the first or second generation. Today, we use I5 or i7, 12th or 13th generation, which are faster. This simple comparison helps us to imagine the computers of 2034. At that time, an i7 intel microprocessor with 64 GB of RAM would be the standard. The hard drives also know a real change. We moved from 128GB HDD in 2014 to 512 GB SSD today.
In the field of electricity, we may have a significant development. According to research, cloud-based operating systems might be prominent in helping to achieve centralized grid management optimization (Yigit et al., 2014). This should be realistic because Ultra-fast and reliable communication networks, possibly leveraging 5G and beyond, connect grid components seamlessly using the Internet of Object Report Office of Energy (energy.gov, n.d.).
Artificial intelligence (AI) will have an impact on the way we manage and distribute electricity. By processing massive amounts of data from various sources, such as weather forecasts, solar panels, wind turbines, and smart meters, AI can optimize the power grid to match supply and demand while reducing greenhouse gas emissions (Mena et al., 2014). Moreover, AI can enhance the security and resilience of the grid by detecting and responding to anomalies, such as cyberattacks, physical sabotage, or natural disasters, that could disrupt the power supply (Hastings, 2023). AI can also improve the reliability and performance of the grid by predicting and preventing equipment failures using sensor data to monitor the health and status of transformers, generators, and transmission lines.
References
energy.gov. (n.d.). Grid controls and communications. Energy.gov. Retrieved March 6, 2024, from https://www.energy.gov/oe/grid-controls-and-communications
Hastings, N. (2023, July 8). Cybersecurity for smart grid systems. NIST. https://www.nist.gov/programs-projects/cybersecurity-smart-grid-systems
Mena, R., Rodríguez, F., Castilla, M., & Arahal, M. R. (2014). A prediction model based on neural networks for the energy consumption of a bioclimatic building. 82, 142–155. https://doi.org/10.1016/j.enbuild.2014.06.052
Yigit, M., Gungor, V. C., & Baktir, S. (2014). Cloud Computing for Smart Grid applications. Computer Networks, 70, 312–329. https://doi.org/10.1016/j.comnet.2014.06.007
No comments:
Post a Comment