High-Fidelity BiLSTM–Informer Hybrid Model for Photovoltaic Power Forecasting with a Novel Real-Time Web Deployment for Enhanced Energy Planning

In this study, a hybrid deep learning model is presented that integrates Bidirectional Long Short-Term Memory (BiLSTM) with the Informer architecture enhanced by ProbSparse attention to improve photovoltaic (PV) power forecasting across multiple time horizons. This integrated design aims to enhance temporal feature representation and reduce long-horizon error accumulation, providing a more reliable and efficient approach to PV power prediction. Existing hybrid models, including Convolutional Neural Network with Long Short-Term Memory (CNN–LSTM) and Gated Recurrent Unit with Long Short-Term Memory (GRU–LSTM), achieved coefficients of determination (R²) of 0.771 and 0.892, respectively; however, their predictive accuracy deteriorated over extended forecasting windows due to cumulative errors and limited sensitivity to long-range temporal dependencies. The proposed BiLSTM–Informer model addresses these limitations through advanced feature engineering, incorporating Fourier transformation, cyclic time encoding, and autoregressive lag selection to improve the representation of periodic and environmental variations. In addition, a comprehensive sensitivity analysis was conducted to examine the effects of varying input sequence lengths (look-back windows) and forecast horizons on model accuracy and temporal learning behaviour. The results show that the BiLSTM–Informer consistently maintained superior predictive stability across extended horizons, achieving up to a 12% reduction in mean absolute error compared with benchmark hybrid models, thereby demonstrating enhanced robustness and generalisation for both short- and long-term forecasting. When evaluated on a five-year hourly PV dataset, the model achieved an R² of 0.952, a mean absolute error of 1.22 kWh, and a root mean square error of 2.21 kWh, outperforming all benchmark approaches. To assess practical applicability, the trained model was deployed via a Streamlit-based web application on the Orender platform, where real-time inference achieved forecasting accuracies ranging from 89% to 97.3% across hourly to monthly resolutions. The dataset and pre-trained model are publicly available at https://github.com/kamilkenny/EDA, and live inference can be accessed at https://kamil-deployment-of-edgehill-durning.onrender.com/.