- August 9, 2023
- Posted by: Cassie Craven
The Spinning Jenny was a ground-breaking invention in the textile industry during the Industrial Revolution. This machine was designed to increase the efficiency of the spinning process, allowing a single operator to spin multiple spindles of thread simultaneously, vastly increasing productivity.
Likewise, Artificial Intelligence is going to transform the energy industry, cutting down the time spent on laborious, intricate trading processes – and doing so with levels of efficiency that humans cannot match.
This article reviews the various ways AI makes life easier for traders and helps them gain the best returns.
How Does AI and ETRM Analytics Improve Energy Trading?
AI plays a crucial role in enhancing the efficiency of energy trading processes through automation, data analysis, and predictive capabilities.
AI-enabled systems analyse data aggregated from a broad array of sources, including data on energy markets, supply and demand trends, consumption patterns, geopolitical events, weather forecasts, and much more. When the algorithms analyse vast bodies of data like this constantly – both historical and real-time data – the most comprehensive insights are possible.
These algorithms produce insights with greater accuracy and speed than was possible before introducing AI into the process. Traders can then make informed decisions and uncover optimal trading opportunities at-scale, and on a moment to moment basis.
One particular form of analytics that is highly advantageous is predictive analytics. The use of historical data to make predictions about future market conditions, energy prices, and demand patterns ensures that traders stay ahead, able to anticipate market fluctuations and make proactive decisions in many contexts.
Below are several applications of this intelligent analytical capability in energy trading.
Risk Management Capabilities
AI-powered energy risk management systems monitor market conditions, assess and quantify risks in real-time, and generate alerts when certain risk thresholds are reached, helping traders to mitigate potential losses effectively. Systems may even suggest risk mitigation strategies.
Optimising Portfolios and Trading Strategies
Another application of analytics is in optimising energy trading portfolios. By considering factors such as expected returns, risks, and correlations between different assets, AI can recommend portfolio adjustments to maximise profitability and manage risk exposure. It may also recommend the best purchase times, and assist with automating hedging and contract negotiation.
AI-enabled trading algorithms are designed to react quickly to market conditions, enabling traders to capitalise on short-term price movements and execute trades at optimal prices. Through analysing real-time market data using intelligent algorithms, these systems can identify trading signals and execute trades with minimal human intervention.
Market Simulation and Scenario Analysis
AI-based models can simulate various market scenarios and assess their impact on energy prices, as well as supply and demand. Traders can use these simulations to evaluate the potential outcomes of different strategies, test the robustness of their portfolios, and make informed decisions going forward.
Algorithms can also assess the performance of numerous trading strategies, indicating which are most effective in certain market conditions.
How Does AI Automate Trading?
As well as its predictive capabilities, AI-enabled software can automate certain elements of trading, such as managing portfolios or carrying out trades. As such, more resources are available to direct towards strategy.
Data Gathering and Processing
As mentioned, AI automatically collects and process vast amounts of data from various sources – a task that would be monumental when done manually on a continuous basis. AI can efficiently handle it, ensuring traders have access to relevant, real-time information at all times.
Another advantage is the use of Natural Language Processing (NLP) to extract and analyse relevant information from unstructured data sources, such as news articles, social media feeds, and regulatory announcements.
The interpretation of textual data provides valuable insights into market sentiment, geopolitical events, and regulatory changes that may impact energy markets.
AI never sleeps – it can continuously monitor the markets, 24/7, detecting anomalies, price movements, and other relevant factors that might impact trading decisions. It is constantly on the lookout and will pick up on information that a human may miss – simply because of the vast number of variables there are to track. In the time it takes for a human to come to the same conclusions, the opportunity would have been missed.
Once a decision is made, AI can automate the execution process. It can connect with trading platforms, place orders, and manage trade settlements, reducing manual effort and potential errors.
AI can help automate compliance tasks by monitoring trade activities, detecting potential violations, and generating reports. Again, Natural Language Processing is vital here – systems can analyse regulatory texts, extract relevant information, and ensure adherence to rules and regulations, saving time and reducing the risk of compliance errors.
How Can AI Improve a Renewable Fuel ETRM?
AI is going to make it easier to manage the changes in the renewables market as demand grows, and this technology is already improving demand management, forecasting and grid operations.
An AI-powered ETRM for renewables may predict the output of renewable sources, improving resource allocation. As a result, producers will have more accurate predictions surrounding the energy they will be able to sell, and likewise for purchasers.
Accurate Models & Robust ETRM Software
Volatile markets require agile, robust, high performing software, especially when it comes to short term trading. However, many traders have relied on models built for thermal generation until now, even though renewables bring about a degree of uncertainty that these models do not account for.
Renewable power generation depends on factors such as weather patterns, wind speed, cloud cover and regional variations, which is one factor making the market less predictable.
In such volatile markets, a lack of insight is a great risk – one that AI-powered ETRM software mitigates against. Also note that cloud based ETRM software is the answer due to the complexity and data processing requirements involved in analysing the markets.
Promoting the Integration of Renewables
The use of AI also promotes the integration of renewable energy into the grid. The algorithms can analyse energy consumption patterns and predict future demand, allowing traders to participate in demand response programs effectively.
Through optimising energy usage and shifting consumption during peak times, AI-powered trading systems can help balance supply and demand, reduce grid stress, and allow traders to capitalise on price differentials.
Making predictions around the energy markets, especially when it comes to renewable energy sources, is a task well suited to AI. AI can optimise trading strategies and mitigate against the risk of loss, and the risk of missed opportunities, its analytical capabilities crunching the numbers on the endless variables that come into the equation.
Price forecasting combined with risk management tailored to the required risk profile gives energy traders the most accurate and timely insights, ensuring the most profitable decisions are made. And as discussed, AI-powered software helps to automate many day-to-day trading tasks.
Inatech is a global provider of cloud based software for energy and commodity trading, with more than 20 years’ experience in international markets. Our solutions include an oil trading platform and a natural gas ETRM. We also provide an ETRM for renewables that supports biofuels trading, biofuels hedging, carbon emission trading and more.
Contact us today to book a demo and discover how a cloud based ETRM system can elevate your trading and risk management operations.