While some still believe that Artificial Intelligence (AI) is a sci-fi vision of the future, it already impacts our everyday lives tremendously. Whether scrolling through your smartphone, finding a route, streaming your favorite TV show or using smart gadgets at home: you are interacting with AI. Almost every tech discussion nowadays is somehow related to AI, and more and more industries are being influenced by it - including the energy sector. In this article, we dig deeper into the concept behind AI, applications in the energy industry, and specifically how it will shape the world of electricity trading.
Artificial Intelligence and its little brother, Machine Learning
The general idea of artificially created life was already described in ancient times. Intelligent artifacts have appeared in both literature and legends since then. The beginning of AI as we know it today is rooted in the work of Alan Turing, who was part of a top-secret group of code breakers helping to break the German Enigma machine during World War II. It became common knowledge with IBM's Deep Blue chess computer, which in 1996 became the first machine to beat the reigning world chess champion Garry Kasparov.
But how do we describe AI today? Unfortunately, there are no single, universally accepted definitions of Artificial Intelligence and Machine Learning; everyone seems to have a different take. Let's give it a try, though!
Artificial Intelligence (AI) in general equips machines with capabilities comparable to the "natural intelligence" attributed to humans and animals. The generic term covers problem-solving methods, including logic and planning procedures, for which human intelligence would be required but which machines, programs, or systems can carry out by collecting and processing data.
The term Machine Learning (ML) is often confused with or even used as a synonym for AI. Although closely related, they are not identical. ML is actually a subfield of AI. It consists of techniques that enable a software program to gain insights from data. Algorithms can recognize patterns and regularities in data sets and develop solutions from them. ML can therefore always be understood as a kind of AI, but not everything that falls under the term AI can be called ML.
So, the approach of AI is to define rules that are applied to a data set and, with time and computational power, lead to a result. With ML, on the other hand, models are presented with as much data as possible so that the machine recognizes the underlying structures and rules itself.
The advantage of AI and ML? They make tasks quicker, more responsive, more automated, and ultimately easier - in a variety of sectors and specifically the energy industry.
How AI is powering the energy industry
Decentralization, the connection of new electrical consumers, and a growing share of fluctuating renewable energies are contributing to the increasing complexity of the energy system. AI can help to handle the enormous data streams that are generated, serving to optimize the system and better satisfy customer needs. Classic application areas include the power grid, power consumption, and electricity trading.
Power grid: AI in the power grid is used to manage a large number of participants with increasing digitalization. Smart grids transport not only electricity but also data, so that network operators receive information on energy production and consumption almost in real time. Intelligent networking, load management, and demand flexibility can thus optimize network utilization. AI can also help to coordinate maintenance activities, minimizing disruptions to network operations as well as costs. A particular focus lies on integrating electric vehicles into the grid by smartly monitoring and coordinating the charging process.
Power consumption: A stabilized grid starts with one of the most important participants – consumers. Smart home solutions that intelligently manage lighting, air conditioning, heating and other electronic devices while measuring consumption in real time through smart meters can make the energy market more efficient. The underlying technology? AI!
Electricity trading: Electricity trading brings together a lot of information in real time, so it is no wonder you will find AI in a variety of applications. For example, generation and demand forecasts can be significantly improved. A large amount of weather and historical data can be systematically evaluated to increase grid stability and thus security of supply.
Moreover, some algorithms are so well-advanced hat they can basically trade for themselves, by monitoring and analyzing trading activities. This is called algorithmic trading, algo trading, or automated trading. With AI solutions, these systems will become increasingly smart, consuming more information, and making decisions based not only on price movements but also on a wider range of data.
Although AI and automated trading are a hot topic, there is still much more to come! So, let’s take a look at what the future might bring and why it will be beneficial for a flexible and secure energy market.
A smarter future for electricity trading
Short-term trading of electricity is becoming increasingly important due to the rising share of volatile generation from renewable energies. The intraday market in particular, in which electricity is traded in the last hours to a few minutes before feed-in to the grid, will play a crucial role in the future.
When the focus lay more heavily on long-term markets, energy traders were quite successful with conventional modeling techniques and trading strategies. However, intraday trading is influenced by factors such as micro weather conditions, meter-level consumption data, as well as cross-border capacity and power plant outages that affect prices and volatility short term. This means that the amount of data and complex interrelationships of information make trading decisions - i.e., what to trade, when, and at what price - more and more difficult. And when we are talking about the amount of data in the intraday market, we mean an astonishing volume, where millions of data points can accumulate in a few hours.
Who thrives on processing large amounts of complex data, finding patterns, and making predictions about the future? As you might guess, the answer is AI.
When we asked Larisa Chizhova, our Principal Data Scientist, about the future of AI and machine learning in the energy market, she answered: "I am certain that we are witnessing a transition from manual to completely automated electronic trading in the energy market right now, similar to what happened in the financial sector 10-20 years ago. And the use of AI and machine learning plays a major role in this transition. We, as humans, can draw only very few conclusions from the data we see, and we easily miss important trends. Finding trading signals in the enormous amount of trading, weather and load data is a task for machines to crunch."
AI-as-a-Service: self-learning trading strategies
At enspired, we are taking the first step by offering intelligent, AI-supported, automated energy trading as a service, 24 hours a day, 7 days a week, 365 days a year. In doing so, we leave behind programmatic approaches limited by human capabilities, using a wide variety of available trading data from relevant sources. Our data scientists are working with cutting-edge AI technology to delegate full decision power to our models, adapting trading strategies to make optimal decisions in milliseconds.
Are you ready to market your assets but not sure how to get started? Or just wanting to avoid the hassles of running your own modern intraday trading desk? Our team of experts in AI-based energy trading can take care of it for you, getting you started in no time with full transparency and no risk.
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