In recent days and years, artificial intelligence (AI) has gained prominence in various industries. However, the definition of the term poses some difficulties. Artificial intelligence’s heart is to make and implement information based on data regardless of the objective set. The terminology distinguishes it from “natural intelligence” allocated to humankind and animals.
The definitions of AI are branches of computing operations that deal with machine learning and the automation of intelligent behaviour of machines and their works. However, the purpose of intelligence remains vague and excludes other research areas, such as robotics linguistics in AI.
Right now, everyone’s lips have AI. It is the fastest-growing branch of the high-tech industry. It is a crucial strategy to harness some of the biggest challenges of recent times, such as pollution and climate change. Establishing a clear differentiation of AI is very difficult or even a precise definition.
Artificial intelligence is often used in connection with synonymous terms of machine learning, deep learning, and big data. These inaccuracies are not the least due to the notion of intelligence, which escapes a clear and unmistakable definition. It can collect data, process it, and produce results. But what exactly is intelligence? Different fields of research have attempted to define intelligence and have come to different conclusions by research.
A central facet of artificial intelligence or AI is that it makes information-based decisions and performs its goals. In some circumstances, this includes gathering information and responding flexibly to changes and the environment. In other words, it means that AI learns from experience and makes new and unique decisions independently by itself. Even with this simple definition of Artificial Intelligence, the terminology remains challenging to understand.
In practice, it is therefore common to speak of strong or weak AI. Strong AI is the Artificial Intelligence in which the application has all facets associated with humankind intelligence; it has the potential to draw logical conclusions, the existence of general knowledge, the possibility to learn to perceive and understand language., plan and predict, move, and manipulate objects and recognize emotions.
Another definition of AI is that “artificial intelligence is a sub-discipline of computing that aims to enable machines to perform tasks intelligently”. Computer science is fundamental in artificial intelligence; It applies to many other fields, such as philosophy, statistics, robotics, linguistics. The transitions are fluid, in particular, because of the inaccuracies of definition.
We have already understood different definitions of AI. Now, let’s take one example of the renewable energy industry and see how AI transforms it. If we talk specifically about the renewable energy sector, Artificial intelligence is acquiring more importance in the energy sector, and it has excellent potential for future energy systems.
Typical application areas are electricity trading, innovative sector coupling of electricity, and transport. The prerequisites for increased use of AI in the energy system are digitizing the energy sector and a large and measurable dataset. Machine Learning is used in artificial intelligence, and it has great importance in the energy sector. However, machine learning and AI are not the same, as machine learning includes some of AI, but not all of it.
Artificial intelligence is present in the field of intelligent networking of consumers and producers of electricity beyond the boundaries of the sector. With the increasing decentralization and digitization of the power grid, AI intensifies more difficulty to manage the number of grid participants and maintain the grid balance of consumers. It requires the evaluation and analysis of a data flow—artificial intelligence helps in processing this data.
Especially with an increasing number of power generation plants such as wind and solar power, it becomes essential for power generation to respond to consumption and vice versa. Artificial intelligence can help assess, control, and analyze the data of different machine learning participants, producers, consumers, and storage facilities connected through the networks.
The integration of electric mobility is a particular goal of AI in the energy sector. An increase in electric cars offers both opportunities and challenges. Still, at the same time, they offer the possibility of storing electricity and stabilizing the grid, for example, by adapting charging demand to price signals and availability. In all of this, AI can help by monitoring and coordinating. In addition to that, artificial intelligence can stabilize the power grid.
We can take examples by detecting consumption, production, or transportation anomalies in near real-time and then developing adapted solutions in machine learning. The first research projects in this area, such as at the Fraunhofer Institute, are already underway. Additionally, AI can help coordinate maintenance work and determine optimal times for individual networks or systems maintenance.
This helps to minimize costs and loss of profit, and disruption in the operation of the network. AI in energy trading helps improve forecasting. It makes it easier to systematically assess a large amount of data in the electricity business, such as weather data or historical data. Better predictions also increase the stability of the network and thus ensure security.
Particularly in forecasting, AI can help facilitate and accelerate the integration of renewables. Machine learning and neural networks play an essential role in improving forecasting in the energy industry. The changes in the quality of forecasts in recent years have shown the potential of AI in this field: There is already a reduction in the demand for control reserve, even though the share of volatile electricity generators in the market has grown.