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While RNNs are great at handling sequences, they sometimes struggle with long-term dependencies . Imagine trying to remember a detail from 20 steps ago—RNNs often “forget” that information. That’s where  LSTM (Long Short-Term Memory) and later BiLSTM (Bidirectional LSTM) models are being used. These models are game-changers for solar power forecasting. Long Short-Term Memory LSTM is an advanced type of RNN designed to remember information for longer periods. It has special structures called gates: Forget Gate: Decides what information to throw away. Input Gate: Decides what new information to store. Output Gate: Decides what the model should output at each step. LSTMs handle long-term dependencies much better than regular RNNs. Since the same dataset was used here as well, you can read more about it in my earlier RNN blog . Model Results for LSTM The training graph shows that the loss decreases steadily as the number of epochs increases. This means the model learns patterns in th...

Introduction When we think about renewable energy, solar power often comes to mind first. But there’s one big challenge: solar energy is not constant . It changes with weather, seasons, and even time of the day. To make solar power more reliable, predicting its future output becomes very important. That’s where machine learning - especially Recurrent Neural Networks (RNNs)  comes in. What is RNN A Recurrent Neural Network (RNN) is a type of deep learning model that is great at handling sequential data —data that comes in order, like time-series or speech. Unlike regular neural networks, RNNs remember past information by looping their outputs back into the network. This makes them perfect for tasks like predicting solar power output, stock prices, or even natural language processing. How RNN Works RNNs take input data step by step.  They store the output of the previous step and use it along with the new input to make better predictions.  This memory-like ability helps th...