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

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. • A deep LSTM network, covering BDLSTM, LSTM and MLP is proposed for hourly forecasting of solar energy. • Automatic time-series decomposition is designed to smooth meteorological variables, addressing the seasonality and trend problems so as to improve the overall accuracy of the model. • To ensure the management of multi-energy systems, cloud-based architecture is established to store and process collected data for energy monitoring, control, and future prediction. [ABSTRACT FROM AUTHOR]