LSTM and BiLSTM Explained: Advanced Deep Learning Techiniques for Time Series Prediction

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 the solar data effectively.

• The LSTM model achieved an R² score of ~0.92, which indicates a strong correlation between the predicted solar power values and the actual values.

• The line graph comparing actual vs. predicted 24-hour ahead solar power values shows that the predicted curve follows the actual curve closely, especially around peak values.

Bidirectional LSTM

BiLSTM takes things a step further. Instead of just looking at past information, it also looks at the sequence from the future to the past. This means it processes data in both directions, giving the model more context and improving accuracy.

Model Results for Bi-LSTM

• The Bi-LSTM training curve shows a smooth decrease in loss across epochs, proving the model learns efficiently without major fluctuations.

• The Bi-LSTM achieved an R² score of ~0.922, slightly higher than LSTM, making it the most accurate model in your comparison.

• Unlike standard LSTM, Bi-LSTM processes data in both forward and backward directions, giving it more context and allowing it to capture complex patterns in solar power fluctuations.

• With its superior prediction performance, Bi-LSTM is highly suitable for real-time renewable energy forecasting, helping grid operators make more reliable scheduling and load balancing decisions. 

Conclusion

In this blog, we saw how LSTM and Bi-LSTM models improve solar power forecasting compared to traditional RNNs.

• LSTM proved effective by remembering long-term patterns in solar generation data, achieving high accuracy (~92%).

• Bi-LSTM went one step further by analyzing data in both directions, giving the best accuracy (~92.2%) and the lowest error values.

Compared to the RNN approach discussed in my previous blog, these models clearly show better learning and prediction ability. Together, they highlight the evolution of deep learning techniques for handling time-series forecasting.