Deep Learning Impact on Short-Term Retail Electricity Price Forecasting

Deep learning (DL) is transforming both day-ahead and real-time retail electricity markets, enabling smarter, faster, and more accurate price forecasting, demand prediction, and trading optimisation.

Day-Ahead Market

In the day-ahead market, where electricity prices are determined 24 hours in advance, DL models such as LSTM, GRU, and Transformer networks analyse vast datasets, historical prices, weather forecasts, renewable generation, load patterns, and policy inputs, to predict price fluctuations and volatility with higher precision.

These models excel at identifying nonlinear dependencies and temporal patterns, allowing retailers and aggregators to bid more strategically, optimise procurement, and reduce imbalance costs. Projects like PriceFM and DeepforKit have shown that hybrid deep-learning frameworks outperform traditional statistical models by 20–30% in accuracy, making day-ahead scheduling more resilient to renewable intermittency and market shocks.

Real-Time Market

In the real-time market, where electricity is traded in 5–15 minute intervals, deep learning enables adaptive forecasting and automated decision-making. Reinforcement learning and streaming deep-learning architectures continuously learn from live data, grid frequency, demand spikes, renewable output, to adjust trading or dispatch strategies instantly.

This real-time adaptability minimises penalties from deviations, enhances grid stability, and supports dynamic retail pricing. Real-time DL models help retailers implement personalised pricing or dynamic tariffs, aligning consumer usage with grid conditions.

Bridging the Gap

Deep learning bridges the gap between forecast accuracy and operational agility, empowering both day-ahead planning and real-time responsiveness in modern retail electricity markets.

References

ScienceDirect · arXiv · IDEAS / RePEc