A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems

Liu, Qi; Darteh, Oscar Famous; Bilal, Muhammad; Huang, Xianming; Attique, Muhammad; Liu, Xiaodong; Acakpovi, Amevi

Abstract

The drive for smarter, greener, and more livable cities has led to research towards more effective solar energy forecasting techniques and their integration into traditional power systems. However, the availability of real-time data, data storage, and monitoring has become challenging. This research investigates a method based on Bi-directional LSTM (BDLSTM) neural network. BDLSTM takes into account the data’s past and future context. The future hidden layer takes input in ascending order while the past hidden layer evaluates the input in decreasing order, making BDLSTM relevant in analyzing the input data’s past context and evaluating future predictions. The eleven-year (2010–2020) weather dataset used for this paper was acquired from NASA. Two pre-processing approaches, Automatic Time Series Decomposition (ATSD) and Pearson correlation, were used to remove the noisy values from the residual components and for feature selection, respectively. To ensure storage and reuse of data, the architecture includes a cloud-based server for data management and reuse for future predictions. Popular in multi-energy systems, the cloud-based server also serves as a platform for monitoring predicted solar energy data. The metrics values and results obtained have demonstrated that the BDLSTM performs efficiently on the available data. Data from two separate climatic horizons proved the study’s quality and reliability.

Citation

Liu, Q., Darteh, O. F., Bilal, M., Huang, X., Attique, M., Liu, X., & Acakpovi, A. (2023). A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems. Sustainable Computing, 40, Article 100892. https://doi.org/10.1016/j.suscom.2023.100892

• Journal Article Type: Article
• Acceptance Date: Jun 19, 2023
• Online Publication Date: Jul 24, 2023
• Publication Date: 2023-12
• Deposit Date: Aug 15, 2023
• Publicly Available Date: Jul 25, 2024
• Journal: Sustainable Computing: Informatics and Systems
• Print ISSN: 2210-5379
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 40
• Article Number: 100892
• DOI: https://doi.org/10.1016/j.suscom.2023.100892
• Keywords: Bi-directional LSTM, Solar energy, Photovoltaic forecasting, Grid-connected PV system, Multi-energy systems
• Public URL: http://researchrepository.napier.ac.uk/Output/3169112