Machine learning models have been widely used for price forecasting in various contexts. Yet, in electricity markets, they often fail to deliver meaningful insights that lead to significant performance improvements beyond analytical models. As the share of renewables grows, alongside the inherent volatility driven by fluctuating demand, weather, and other external factors, effective forecasting of electricity prices is becoming increasingly important. Accurate price forecasts enable market participants to optimise bidding strategies and improve resource allocation, ultimately enhancing the operational efficiency of energy assets.
Research conducted in 2021 and 2022 by OptiGrid’s founders introduced unique methodologies for electricity price forecasting, focusing on the importance of effective data processing and the use of fit-for-purpose models when using machine learning. A key takeaway of the research is the critical role of data pre-processing and the need for models specifically tailored to each market’s unique conditions.
The Australian National Electricity Market (NEM), known for its significant price volatility, presents unique forecasting challenges. The research shows how tailored filtering and processing techniques during model training can lead to more accurate predictions, particularly for capturing price dynamics across different quantiles. Additionally, ensemble forecasting methods and quantile regression averaging demonstrated superior accuracy compared to traditional point forecasts.
The research further emphasises that no single model performs best under all conditions. By utilising a combination of models trained on different time windows, market participants can ensure better adaptability during both stable and volatile periods. This multi-model approach enhances forecast robustness across various market conditions.
By applying well-processed data and fit-for-purpose models, energy market participants can improve decision-making, optimise resource management, and increase overall market efficiency.
You can read the full text of the paper here. If you’d like to learn more about our research and price forecasting solutions, and how they can support your business, feel free to reach out to us. We’d love to hear from you!
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