Short-term market clearing price forecasting in India’s Green Day-Ahead Market using a hybrid wavelet–XGBoost model

Accurate short-term forecasting of the Market Clearing Price (MCP) is essential for the efficient operation of electricity markets with increasing renewable energy penetration. This challenge is particularly significant in India’s Green Day-Ahead Market (GDAM), where quarter-hourly trading and the variability of renewable generation introduce complex nonlinear price dynamics. This study presents a hybrid Wavelet–XGBoost framework for short-term MCP forecasting by integrating the discrete wavelet transform with the Extreme Gradient Boosting (XGBoost) algorithm, where the wavelet family and decomposition level are selected empirically through a systematic sensitivity analysis rather than assumed a priori. The wavelet decomposition separates the MCP series into multiple frequency components, enabling the model to capture both long-term trends and rapid price fluctuations while reducing the influence of noise. The proposed framework is evaluated using the 2024 GDAM dataset (32,159 valid quarter-hourly observations after data-quality screening) and compared with Naïve, Random Forest, LightGBM, conventional XGBoost, and an LSTM benchmark. On the held-out test period, the proposed model achieves an MAE of 258.6 Rs/MWh, RMSE of 523.7 Rs/MWh, RRMSE of 12.83% and MAPE of 5.65%, and attains the lowest RMSE and RRMSE of all evaluated models. Diebold–Mariano and Wilcoxon signed-rank tests confirm that the proposed model achieves significantly lower squared error than Naïve, Random Forest, XGBoost and LightGBM, and significantly lower typical-case absolute error than the same-architecture XGBoost and LightGBM baselines, while a tail-performance analysis quantifies the systematic underprediction of all models near the regulatory price cap. Furthermore, SHAP-based interpretability analysis identifies lagged MCP and bidding-related variables as the most influential factors affecting price prediction. The proposed framework provides a computationally efficient and interpretable forecasting approach for short-term MCP prediction in India’s GDAM, with particular strength in reducing larger forecasting errors and supporting bidding strategies, market scheduling and renewable energy integration.