Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 3; p. 882
• Main Authors: Herrera-Casanova, Reinier, Conde, Arturo, Santos-Pérez, Carlos
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.01.2024
• Subjects: Accuracy; Algorithms; Alternative energy sources; Bayesian optimization; bidirectional long short-term memory (BiLSTM); Datasets; Mathematical optimization; Neural networks; Optimization algorithms; photovoltaic power forecasting; predictions intervals; Solar energy; Solar energy industry; Time series; Weather forecasting; bidirectional long short-term memory (BiLSTM); predictions intervals; Bayesian optimization; photovoltaic power forecasting
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24030882

Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from a small PV installation located at the Polytechnic School of the University of Alcala. To improve the quality of historical data and optimize model performance, a robust data preprocessing algorithm is implemented. The BiLSTM model is synergistically combined with a Bayesian optimization algorithm (BOA) to fine-tune its primary hyperparameters, thereby enhancing its predictive efficacy. The performance of the proposed model is evaluated across diverse meteorological and seasonal conditions. In deterministic forecasting, the findings indicate its superiority over alternative models employed in this research domain, specifically a multilayer perceptron (MLP) neural network model and a random forest (RF) ensemble model. Compared with the MLP and RF reference models, the proposed model achieves reductions in the normalized mean absolute error (nMAE) of 75.03% and 77.01%, respectively, demonstrating its effectiveness in this type of prediction. Moreover, interval prediction utilizing the bootstrap resampling method is conducted, with the acquired prediction intervals carefully adjusted to meet the desired confidence levels, thereby enhancing the robustness and flexibility of the predictions.