Electricity Spot Market Clearing Price Prediction Model: Algorithm Research and Application Based on Time Series Analysis

Abstract

Against the backdrop of high-penetration renewable energy integration and increasingly intelligent power communication infrastructures, electricity spot market prices exhibit complex nonlinear fluctuations that directly affect the operational costs and scheduling efficiency of energy-intensive industrial systems. Accurate price forecasting is therefore essential for coordinated energy management and reliable information transmission in modern smart grids. This paper proposes a time-series prediction algorithm integrating fractal analysis and deep learning to capture both long-term memory characteristics and local abrupt variations in electricity price sequences. The method first quantifies long-range correlations using the Hurst exponent derived from rescaled range analysis, then extracts multi-scale fluctuation features through multifractal detrended fluctuation analysis, and finally embeds fractal characteristics into the loss function of a Long Short-Term Memory (LSTM) network to construct a fractal-aware prediction model. A dualwindow strategy is adopted to reconcile the statistical requirements of fractal estimation with temporal responsiveness. Experimental results based on actual clearing data from five domestic spot market pilot regions demonstrate that the proposed model consistently outperforms conventional ARIMA, standard LSTM, VMD-LSTM, CEEMDAN-BERT-LSTM, and Transformer-based methods in both MAPE and RMSE. The proposed framework provides an effective decisionsupport tool for intelligent energy management and offers potential reference value for signal-aware forecasting and information processing in advanced electromagnetic and communication-enabled power systems.