Use of Deep Learning Models in Solar Power Generation Forecasting

Reference

• Researchers at the Indian Institute of Engineering Science and Technology, Kolkata have developed an improved AI technique to forecast solar power generation.
• Instead of using a simple deep learning model, these scientists used a group of deep learning models. It is more advanced than the simple deep learning model and gave high accuracy forecasts of solar power generation. 

Deep Learning Model

• Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a manner inspired by the human brain.
• Deep learning models can recognize complex patterns in images, text, sounds and other data to provide accurate information and predictions.
• Deep learning methods can be used to automate tasks that typically require human intelligence, such as describing images or transcribing a sound file into text.
• Deep learning technology drives many AI applications used in everyday products, such as:
  • Digital assistants
  • Voice-activated television remotes
  • Fraud detection
  • Automatic facial recognition
• Deep learning models are computer files that have been trained by data scientists to perform tasks using algorithms or predefined sets of data. Deep learning models are used in a variety of businesses to analyze data and make predictions in a variety of applications. 

Benefits of Deep Learning Models in Solar Power Generation Forecasting 

• LSTM Models: Deep learning models ranging from simple Artificial Neural Networks to complex Long Short-term Memory (LSTM) are improving forecast accuracy.
  • LSTM is a particularly effective architecture for forecasting based on sequential data.
• Ensemble Models: This machine learning approach combines multiple other models in the prediction process and then synthesizes the results into a single score or spread. This improves the accuracy of predictive analytics and data mining applications.
  • The model incorporates physical characteristics of solar panels that increase forecast accuracy, such as parameters such as the number of cells in the panel, the maximum operating temperature of the panel, the type of material, and the ambient temperature.
• Bi-directional Long Short-term Memory (BI-LSTM): It is a type of Recurrent Neural Network (RNN) designed to handle sequential data.
  • Unlike standard LSTM, BI-LSTM processes data in both directions (past to future and future to past).
  • The researchers created a dataset by combining weather parameters and solar power generation data and then enriched the dataset by bringing in weather data along with physical characteristics of solar panels installed in the respective solar plants.
    • The BI-LSTM model can predict the future solar power generation of a specific solar plant on both short and long horizons, regardless of the geographic location of the solar plant.
• For short-term forecasting it can predict solar power generation from fifteen minutes to one hour in advance and for long-term forecasting it is able to predict power generation 1 to 3 days in advance with remarkable accuracy.
• This model also performed better than the base model for long-term forecasting. 

Conclusion

Most of the deep learning models available for solar power forecasting usually use only weather parameters to predict solar power generation but in this model some additional features were generated using the physical properties of solar panels. These are different and more useful than the latest models.