Artificial Intelligence and Security Challenges

Abstract

Big data processing, vast computing power, information technology, improved machine learning (ML) and deep learning (DL) algorithms are driving the recent growth in AI technologies. With more conventional methods, Google would not have been able to reduce its field device management costs by 40% as much as it has by deploying deep-mind AI technologies. The energy sector can benefit from AI technology by utilizing the expanding opportunities that result from the use of the Internet of Things (IoT) and the incorporation of renewable energy sources. Supercomputers, power electronics, cyber technologies, information, and bi-directional connectivity between the control center and equipment are only a few of the sophisticated infrastructures available to the smart energy sector. The infrastructures of the current electricity system are too old, ineffective, outdated, unreliable, and do not offer enough protection from fault circumstances. But energy production, distribution strategy, and financial sustainability are crucial for the world economy. The integration of renewable energy sources was not intended to be managed by the conventional power system (RES). Meeting the fluctuating demands of the power system is made more difficult by changes in the characteristics of RES (such as wind, solar, geothermal, and hydrogen). The energy sector is undergoing a change thanks to recent developments in AI technologies, such as machine learning, deep learning, IoT, big data, etc. Many nations have implemented AI technology to carry out many types of jobs, including managing, predicting, and effective power system operations. Photovoltaic (PV) systems may be controlled effectively by inverters thanks to, which also improves the ability to track power points. Artificial maximum power point tracking (MPPT) techniques are efficient and can improve performance compared to conventional MPPT techniques. Due of its simplicity and speed of calculation, particle swarm optimization for MPPT is preferred by swarm intelligence classes Predictive technologies are frequently used to anticipate load demand, electricity costs, generation from RES (such as wind, hydro, solar, and geothermal energy), as well as fossil fuels (such as oil, natural gas, and coal). Probabilistic forecasting (forecasting future events, for example) and non-probabilistic forecasting (forecasting fuel purchase management, generation planning, distribution scheduling, various forms of investment programs, maintenance schedules, and security purposes) are both possible.