Energy Prices Forecasting on the SPOT Market Using AI

 

With the increasing share of renewable energy sources (RES) in the energy mix and in the face of the growing importance of the SPOT market, energy companies, RES developers, and investors are increasingly looking for modern tools that will enable them to effectively manage the production, distribution, and sale of energy. Artificial intelligence (AI) turns out to be a great solution for the energy sector, enabling precise energy price forecasting and optimizing energy storage management strategies.

Price Volatility on the SPOT Market

 

The SPOT market, a short-term market where electricity is traded on an ongoing basis, is becoming more and more important. The growing interest in this market results from its flexibility and ability to respond to rapid changes in energy supply and demand.

On the other hand, price volatility on the SPOT market, resulting from the instability of RES, is challenging for both energy companies and end consumers. Energy prices on this market can change rapidly depending on weather conditions, for example, which makes precise production and sales planning difficult.

Energy prices forecasting on the SPOT market in such a dynamic environment is becoming a key element of energy companies’ strategies. It requires taking into account many variables, such as demand, availability of RES, as well as external factors, such as energy policy and the geopolitical situation.

Traditional forecasting methods, based only on the analysis of historical data, become insufficient in the face of rapidly changing market conditions. Artificial intelligence turns out to be a breakthrough, offering modern solutions that allow for more accurate prediction of short-term price fluctuations.

The Challenges of RES Instability

 

Renewable energy sources, such as wind and solar energy, are characterized by irregular production that depends on changing weather conditions. For example, wind farms can produce significant amounts of energy during periods of strong winds, but on calm days, their production drops to zero. Similarly, photovoltaic installations are dependent on insolation, which makes their efficiency variable depending on the time of day and weather conditions.

While RES play a key role in the energy transition, their instability is a significant challenge for grid operators, energy companies, and end users. On the SPOT market, where prices are set in real-time, even small fluctuations in supply and demand can lead to significant price changes.

In moments of overproduction, energy may be lost, and in moments of shortage, prices on the SPOT market may increase rapidly. This may lead to financial instability for energy companies and increased costs for consumers. Another consequence can be power outages.

Proper forecasting is crucial to ensuring the stability of the energy system. For this reason, it is so important to implement modern technologies that will allow for better management of these variables and minimize risk.

The Importance of Energy Storage in Stabilizing Energy Systems

 

Energy storage plays a key role in stabilizing power grids, especially in the context of unstable RES. Batteries, accumulators, and other energy storage technologies allow for the gathering of surplus energy during periods of high production and its return to the grid during periods of low production. This allows for maintaining grid stability and minimizing the risk associated with price fluctuations on the SPOT market.

Artificial intelligence can support the optimization of energy storage. Thanks to price forecasts, AI-based systems can help make decisions about when to store energy and when to sell it. For example, in the case of a predicted increase in energy prices on the SPOT market, the system can decide to store energy in order to sell it when prices reach a higher level. This approach allows for maximizing the profits from energy storage.

The Role of Artificial Intelligence in Energy Prices Forecasting

 

Artificial intelligence is becoming an increasingly popular tool in forecasting energy prices on the SPOT market. Thanks to advanced techniques such as neural networks, machine learning algorithms, and big data analysis, AI is able to analyze huge amounts of data from various sources, such as weather forecasts, historical energy production data, data on previous prices, information on energy demand, data from sensors in the power grid, market data, and other variables.

Neural networks can recognize patterns that are difficult to detect using traditional statistical methods. For example, they can be used to forecast energy prices based on predictions of renewable energy production or the impact of weather conditions on that production, allowing companies to better plan their operations.

Machine learning algorithms are able to analyze correlations between different market factors. They allow for automatic adjustment of models to changing market conditions, which is especially important on the SPOT market, where price volatility is high. For example, they can be used to identify which variables have the greatest impact on energy prices at a given moment, making it easier to predict future price fluctuations.

Big data analytics enables the processing and analysis of data to draw conclusions about future price trends. It allows for the creation of more complex and precise forecasting models that take into account a wide range of variables.

The Benefits of Accurate Energy Prices Forecasting with AI

 

With its ability to identify complex patterns and correlations in data, AI enables the following:

• More accurate price forecasting – AI algorithms can take into account a range of factors that affect energy prices, such as weather forecasts, energy demand, fuel prices, and geopolitical events.
• Better production planning – accurate price forecasts enable energy companies to optimize production, avoiding situations where too much or too little energy is produced.
• More efficient use of energy storage – thanks to accurate forecasts, energy storage can be charged and discharged at optimal times, maximizing profits from price arbitrage.
• Reduction of operating costs – accurate forecasts enable optimization of production processes and energy distribution, which leads to reduced operating costs.
• Minimizing energy purchase costs – energy consumers can plan purchases on the SPOT market more effectively, avoiding situations in which they have to buy energy at high prices.
• Maximizing profits – both energy producers and consumers can increase their profits by better managing market risks and optimizing their operations.
• Accuracy, speed, adaptability – AI models are able to quickly process large amounts of data and adapt to changing market conditions, ensuring high forecast accuracy.
• Social and environmental impact – more accurate energy price forecasting can contribute to increased energy efficiency and accelerate the energy transition.

Solar Spy – AI-Based Short-Term Photovoltaic Farm Production Forecasting for Energy Trading

 

Created by Pragmile, Solar Spy is a cutting-edge SaaS platform for managing photovoltaic installations. It uses proprietary artificial intelligence models to optimize the operation of solar farms, reduce costs, increase efficiency, and provide a single source of truth for all stakeholders. The Forecasting module available in the software enables accurate forecasting based on the actual performance of the photovoltaic farm for the purpose of energy trading. This means that it can be a tool supporting price forecasting on the SPOT market.

The module provides detailed and precise forecasts of energy production from solar panels. Since the amount of energy produced from the installation depends on weather conditions, predicting the level of production based on such data is crucial for forecasting the supply of energy on the market. Thanks to the Solar Spy module, energy companies can predict changes in energy production in advance and better adjust their activities on the SPOT market, which allows them to manage the risk associated with price volatility.

Forecasts can be used to optimize purchasing and sales strategies on the SPOT market. For example, if forecasts show that production will increase in the coming hours, the energy company may decide to sell energy early, before market supply increases and prices drop. Similarly, in periods of predicted production declines, the company may hold off on selling and wait for prices to rise when demand increases.

Solar Spy forecasts can also support energy storage management decisions. During periods of anticipated excess production, companies can store energy to use it later when prices on the SPOT market rise. Thanks to precise forecasts, it is possible to maximize profits by selling energy at the most favorable moments.

By accurately predicting the level of production from photovoltaic installations, the Forecasting module allows companies to better manage the instability of renewable energy production, resulting in greater revenue stability. Knowing ahead of time what the changes in production will be, they are able to better respond to price volatility on the SPOT market, avoiding sudden increases in costs or decreases in prices, which may adversely affect their financial performance.

If you want to learn more about energy price forecasting based on artificial intelligence and discover the potential of tailor-made solutions for your business, schedule a free consultation with Marcin Jabłonowski – Managing Director and AI Solutions Architect at Pragmile.