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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 …