Accurate forecasting in energy: How AI is changing decision-making
The development of advanced models for predicting energy consumption and production brings new opportunities – but also new questions. Machine learning tools are not a "miracle solution" that will solve all problems in the energy sector. Their actual effect depends on the quality of the data, appropriate settings, and, above all, the environment and tasks for which they are used.
That is why it is important to test these models on your own data and verify whether they bring added value in a specific prediction task. Their flexibility – different choices of architecture, parameters, or input data – allows them to better respond to the growing complexity and volatility of the energy market. In an environment where the amount of data is constantly growing and the structure of sources and consumption is changing, we will probably not be able to do without such tools in the future. At the same time, however, it is necessary to continuously monitor their performance, compare them with traditional methods, and evaluate their benefits.
Although the improvement in accuracy is often "only" in the order of a few percent, even such a change can represent a real financial advantage in the energy sector. At the same time, however, the benefits of advanced models may not lie solely in their accuracy. In many cases, they offer simplification and automation of the prediction process, reducing the need for manual intervention and freeing up analysts' capacity for interpreting results and making strategic decisions.
No model can stand without quality data
The quality of predictions is always limited by the quality of available data. This applies to both traditional statistical methods and modern machine learning models. The basis is to have:
- long-term and well-structured historical data,
- supplementary external sources (weather, market indicators),
- properly cleaned and consistent data series.
Machine learning models are even more sensitive to data — they can reveal subtle patterns, but only if they have something to draw on. If the data is not of high quality, the models cannot fully exploit their potential. That is why data preparation is often more crucial than the choice of model itself.
Market flexibility drives up the amount of data and demand for predictions
The energy sector is undergoing rapid change. The increasing share of decentralized sources, electromobility, aggregation of flexibility, and active work with battery systems are leading to dramatic growth in the number of measurable points and the volume of data. Each new source and each new flexible element adds further variability — and thus another layer of complexity that needs to be predicted. Traditional models with a limited number of input variables are often insufficient to capture this dynamic. Advanced models make it possible to:
- incorporate a larger amount of input data,
- test different scenarios more quickly,
- respond flexibly to changes in portfolio structure.
This approach increases the reliability of predictions in an environment where conditions are constantly changing.
Stronger market position thanks to more accurate predictions
Modern prediction methods give traders and portfolio managers much more solid ground to stand on. More accurate short- and medium-term estimates enable informed decisions to be made at the moment when the outcome matters most. Thanks to the automation of part of the analytical work, there is more room for the actual trading strategy and monitoring of market signals.
Another major advantage is the ability to work with a combination of multiple models, which increases the stability of results and reduces the risk of wrong decisions. Trading teams can quickly respond to changes in weather, fluctuations in production, or the current market situation — and adjust their position to minimize risk and maximize returns. Even a small improvement in the accuracy of predictions can translate into millions for companies operating with large volumes, making accurate predictions a strategic advantage rather than just a technical tool.
Chcete vědět, kdy se pokročilé modely vyplatí i ve vašem prostředí?
V rámci našeho výzkumně-vývojového projektu jsme připravili webinář, který shrnuje klíčové poznatky z experimentů nad reálnými daty a ukazuje, jak různé modely fungují v praxi. Pokud chcete zjistit, jaký přínos mohou mít pro vaše konkrétní portfolio, nabízíme také možnost studie na vlastních datech. Získáte přehled o tom, zda moderní modely přinášejí ve vašem prostředí reálné zlepšení – a v jakém rozsahu.
Stále váháte? Zde jsou další důvody, proč si webinář pustit:
Získáte aktuální pohled na využití AI v energetických predikcích přímo z praxe.
Uvidíte reálné výsledky experimentů a práci s daty.
Nahlédnete do budoucích plánů integrace AI modelů v rámci Lancelot FMS.
