Short-Term Rooftop Photovoltaic Power Forecasting for Energy System Management: A CMDHOLE-Optimized Transformer-LSTM Framework

Abstract

Accurate short-term rooftop photovoltaic (PV) power forecasting is important for energy system management because forecasting errors directly affect local scheduling, reserve coordination, and distributed PV balancing under rapidly changing meteorological conditions. This paper proposes a CMDHOLE-Transformer-LSTM framework for deterministic one-step-ahead forecasting of aggregated rooftop PV power in a campus-level distributed PV setting. The model combines a Transformer module for global dependency extraction, a long short-term memory (LSTM) module for temporal-memory refinement, and a Cauchy-mutation-improved DHOLE (CMDHOLE) algorithm for hyperparameter optimization. Experiments are conducted on the public Hong Kong University of Science and Technology rooftop PV dataset, in which outputs from multiple rooftop PV stations are aggregated into a single campus-level PV power series. Historical PV power and eight meteorological variables are used as inputs under a chronological 80%/10%/10% training-validation-testing partition. Because nighttime and near-zero PV samples are retained, percentage-based metrics are excluded, and performance is evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R2). The proposed model achieves a test-set MAE of 25.4631 kW, an RMSE of 57.8833 kW, and an R2 of 0.96657. Relative to the strongest non-proposed baseline, Extreme Gradient Boosting, MAE and RMSE are reduced by 7.37% and 3.45%, respectively. The ablation and optimizer-comparison results further show that both the hybrid Transformer-LSTM backbone and the CMDHOLE optimization strategy contribute meaningfully to the final forecasting performance. Overall, the proposed framework provides an effective solution for short-horizon rooftop PV forecasting in campus-level distributed energy-management applications.