A Short-Term Load and Photovoltaic Power Forecasting Method Based on Robust Data Preprocessing and QPSO-BiLSTM

Accurate short-term load and photovoltaic (PV) power forecasting is essential for power-system dispatch and renewable-energy accommodation under high PV penetration. However, measured source-load data often contain outliers and missing values, while the forecasting performance of bidirectional long short-term memory (BiLSTM) networks is sensitive to hyperparameter selection. This paper develops an integrated short-term load and PV power forecasting framework that combines characteristic-aware robust data preprocessing with BiLSTM modeling tuned by quantum particle swarm optimization (QPSO). An iterative 3σ criterion is first used to detect spike, drop, negative, and flat anomalies according to the different characteristics of load and PV data, and piecewise cubic Hermite interpolation is then applied to repair abnormal and missing points. A BiLSTM network is used to capture bidirectional temporal dependencies, and QPSO is introduced to optimize the number of hidden units, maximum training epochs, and learning rate. Experiments were conducted on measured load and PV datasets with a 15 min resolution, and the proposed model was compared with long short-term memory (LSTM), BiLSTM, and a BiLSTM tuned by particle swarm optimization (PSO), denoted PSO-BiLSTM. In the primary forecasting evaluation on measured grid data, QPSO-BiLSTM achieved mean absolute percentage error (MAPE) values of 2.1616% for load forecasting and 7.5687% for PV power forecasting. Additional random-seed-controlled ablation and robustness validation confirmed statistically significant contributions of the preprocessing procedure and QPSO to load forecasting, while the proposed framework maintained competitive performance for PV forecasting. The proposed method improves forecasting accuracy and provides reliable data support for short-term operation and dispatch of renewable-energy power systems.