Solar Energy Prediction Using Recurrent Neural Networks (RNN)

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 them capture patterns over time.
• However, RNNs also face problems like vanishing and exploding gradients during training, which can reduce their accuracy on long sequences.

DataSet

For training the RNN model, I used historical solar power generation data. The dataset included time-series records of photovoltaic (PV) output values measured over a period of time. These values helped the model learn how solar power changes throughout the day and across different conditions. Since the focus of the project was on time-series prediction, only the past solar power values were considered as input features to forecast future output.

Model Results

• I have used RNN to train a model that can forecast 24-hour ahead photovoltaic (PV) power generation. By feeding the model historical solar power data, RNNs learned the patterns and predicted future outputs.
• The plot between model loss and epochs shows how the error decreases as training progresses. This tells us that the model is learning better representations of the data.
• This plot helps us identify at which epoch the model performs best.
  
  

• Too few epochs lead to underfitting.
• Too many epochs leads to overfitting (model memorizes data instead of generalizing).

Performance Validation: Actual vs Predicted Results

• The scatter plot compares the actual solar power output values with the predicted values from the RNN model. Each dot represents a data point.
• The R² value shows how well the predicted values match the actual values. A score of 0.894 indicates that the model explains almost 89.4% of the variation in solar power output.
• Ideally, if predictions were perfect, all points would lie exactly on a 45° diagonal line. The scatter plot shows that most points are clustered around that line, meaning the model performs well.

Scatter plot for actual and predicted values

• Such plots are important in energy forecasting tasks because they visually prove whether the model can be trusted for future predictions.

Conclusion

Recurrent Neural Networks (RNNs) give us a smart way to handle time-based data like solar power output. They learn patterns from the past and use them to predict the future, making them very useful in energy forecasting. In our case, the RNN model achieved an accuracy of nearly 89%, showing that it can capture important trends in solar energy production. While newer models like LSTM and BiLSTM perform even better, RNNs are still a solid starting point for understanding and solving problems related to time-series prediction.