Definition
In recent years, AI has gained importance in a wide variety of sectors. But, defining the term poses some problems. Central to AI is that it makes and performs decisions based on data independently with regard to the set aims. The term AI from the "natural intelligence" is attributed to people and animals. Narrower descriptions see AI as a branch of computer science that deals with ML and the automation of intelligent behavior. Still, the meaning of Intelligence continues vague and excludes other research areas, such as robotics or linguistics.
AI: the Attempt at Differentiating
AI is on everyone's lips right instantly. It is the fastest-growing part of the high-tech industry in companies like www.Optum-eba.de. The German government sees Artificial Intelligence as a key approach for mastering some of the most significant challenges of our time, such as climate change and pollution.
It isn't easy to set a clear differentiation of AI or even a specific definition. Artificial Intelligence is often used in association or sometimes even synonymous with ml, big data, or deep learning. These errors are not least due to the idea of Intelligence, which eludes an unambiguous meaning.
AI is clearly distinguished from "natural intelligence" which is connected to humans and animals. AI is the Intelligence of, for example, machines, algorithms, programs, applications, or systems. These can get data, process it, and provide results.
But what is Intelligence specifically? Various areas of research have already ventured to define Intelligence - and have come to various conclusions. A central character of Intelligence in Artificial Intelligence is that it makes choices based on the information and carries out activities with regard to its aims. Under certain conditions, this includes collecting this data and reacting flexibly to changes and the atmosphere. In other words, this indicates that Artificial Intelligence learns from experience and makes new choices independently.
Even with this sense, the term continues challenging to grasp. In use, therefore, it is fair to speak of powerful or weak AI. A strong Artificial Intelligence is one in which the application has all features associated with personal Intelligence, such as the capacity to draw logical conclusions, the presence of general knowledge, the experience to learn to perceive and understand language, to prepare and foresee, to move and manipulate things, and to recognize emotions.
Another standard definition of Artificial Intelligence is that AI is a subdiscipline of computer science that enables machines to perform tasks effectively. Although computer science is essential in AI, Artificial Intelligence applies to other fields, such as statistics, robotics, linguistics, or philosophy.
There is still a conversation about what is already considered Artificial Intelligence and what is still the computing ability of machines. The changes are fluid, not least because of the definitional errors.
Machine Learning
The term ML is often used in combination with Artificial Intelligence and is of great value in the energy industry. But, ML and AI are not the same since ML includes a part but not all of the Artificial Intelligence. ML means that machines can learn individually, i.e. draw results for the future from their actions and solve problems that have not been there previously.
AI in the Energy Industry
AI becomes more and more critical in the energy business and is having great potential for the ultimate design of the energy system. Common application areas are electricity trading, smart grids, or the area coupling of electricity, heat and transport. Requirements for an increased use of Artificial Intelligence in the energy system are the digitalization of the energy sector and a correspondingly huge set of valid data. Artificial Intelligence helps make the energy industry more effective and secure by analyzing and evaluating the data volumes.
AI in the Power Grid - Smart Grids and Sector Coupling
In particular, Artificial Intelligence is present in the intelligent networking of electricity customers and generators over sector boundaries. With the growing decentralization and digitalization of the power grid, it is more difficult to manage many grid participants and keep the grid in balance. This needs assessing and analyzing a flood of information. AI helps process this data as fast and efficiently as reasonable.
Smart networks are another area of use. These networks bring not only electricity but also information. Particularly with an increasing amount of volatile power production plants such as solar and wind, it is growing more and more necessary for power production to react intelligently to consumption (and vice versa). Artificial Intelligence can help evaluate, investigate, and control the information of the various participants connected to each other via the grid.
A special focus of Artificial Intelligence in the energy industry is on the combination of electro mobility. An improvement in e-cars offers possibilities and challenges. The charging of electric cars must be organized, but at the same moment, they provide the option for storing electricity and supporting the grid, for example, by changing the charging demand to price signals and availability. Artificial Intelligence can help with all this by monitoring and coordinating.
In addition, Artificial Intelligence can support the power grid by, for example, detecting irregularities in generation, consumption, or transmission in near real-time and then develop proper solutions.
Further, Artificial I can help correspondent support work and determine optimal times for the maintenance of networks or unique systems. This helps reduce costs and loss of earnings as well as disturbances of the network operation.
Artificial Intelligence in Electricity Trading
AI in power trading benefits improves forecasts. With Artificial Intelligence, it is easier to systematically evaluate a large amount of data in electricity trading, such as weather data or historical data. More reliable forecasts also improve grid stability and thus provide security. Particularly in the field of projections, Artificial Intelligence can help facilitate and speed up the integration of renewables. ML and Neural Networks play an essential role in improving predictions in the energy industry.
In recent years, improvements in forecasting property have shown the potential of Artificial I in this area: There is now a reduction in the demand for power reserve, even though the share of volatile power generators in the market has grown.
Artificial Intelligence for Power Consumption
Clients intelligently related in the electricity system can commit to a stable and green electricity grid. Innovative home solutions and smart meters now exist, but they are not yet broadly used.
In an intelligent networked home, the networked devices react to prices on the electricity market and adapt to home usage patterns in order to save electricity and decrease costs. One example is intelligent networked air conditioning methods. They respond to prices on the electricity market by increasing their output when electricity is plentiful and cheap. They can also include data about user choices and time windows in their calculations by analyzing user information.
AI in the Energy Industry - Obstacles and Criticism
Any AI is only as intelligent as its information. This is one of the most significant sticking details. The topics of data protection and data security are some of the most significant weak points for the use of AI.
Energy equipment and the entire energy scheme are part of this critical infrastructure. This is why cybersecurity is growing more and more important today and in the future in order to protect the extremely networked power grid from attacks and information theft from the outside. There are now strict security conditions for participants in the electricity market in data protection and data security, though.
Contrary to the popular opinion that Artificial Intelligence makes the power grid less secure, AI can fight against cyberattacks. It can instantly check large volumes of information and thus detect differences. Artificial Intelligence can also conclude past cyberattacks. ML has already achieved great progress in this area, for instance, in Trojans' detection and defense.
Many end users are important of AI, especially about smart home technologies. This is normal because the data of the most individual space reveals a lot about its users. Researches have shown that the biggest obstacle to accepting smart meters is fear of disclosing private information without understanding exactly how it is used. These fears are justified, as there is still no control on how to handle this sensitive data, which is essential for the electricity system of the prospect.
Germany and the EU are working to curb data path by private organizations, as is occurring in the USA and China, for instance.
To give the energy business and end consumers more confidence in the Artificial Intelligence, it must be clearly communicated how the information is used and by whom, and data security must be ensured.
Another study of Artificial Intelligence is the power consumption of AI itself. The processing of large volumes of data consumes a lot of power. When using Artificial Intelligence for energy system change, it is crucial to analyze how to design the data centers themselves to be energy-efficient and as climate-neutral as potential. Possible solvents to this dilemma include the physical closeness of data centers and renewable energy generation plants, the suspension of power-intensive computing operations to times when a lot of power is possible, more energy-efficient IT hardware, or programming that needs as little computing power as feasible.
Artificial Intelligence offers many suitable application situations in the energy business that will support the energy transition and a climate-friendly energy system. However, it will be important to protect user data and make Artificial Intelligence transparent and understandable.
#GlobalAIandDataScience#GlobalDataScience