For forecasting electricity generation from fickle renewable energy sources like wind and solar, there is help coming from artificial intelligence. Machine learning and (its subset) deep learning are beginning to replace conventional, weather and satellite data based forecasting or statistical prediction models.

Deep learning models — from simple artificial neural networks to complex ‘long short-term memory’ (LSTM, which is an architecture particularly effective for making predictions based on sequential data) — are coming into play, improving the accuracy of forecasting.

Now three researchers from the Indian Institute of Engineering Science and Technology, Kolkata, Rakesh Mondal, Surajit Kumar Roy and Chandan Giri, have come up with an improved AI technique for forecasting solar generation. Instead of using a simple deep learning model, these scientists employed an ensemble of deep learning models, which they describe as “one step more advanced than simple deep learning models.” The result, they say, is higher accuracy.

The AI advantage

Not that ensemble models, which combine predictions from multiple individual deep learning models, are entirely new. In a paper published in Energy, the authors acknowledge that other researchers have tried the ensemble model method but say that they have “included features that enhance accuracy of prediction” in their own research. These features include parameters like physical characteristics of solar panels including the number of cells in a panel, the maximum working temperature of the panel, the material type and ambient temperature. “None of the existing techniques has considered these parameters for solar power prediction,” the authors say.

Mondal, Roy and Giri have used a technique called ‘Bi-directional Long Short-term Memory’ or BI-LSTM — a type of recurrent neural network (RNN) designed to handle sequential data. Unlike standard LSTM, which processes data in one direction (past to future), BI-LSTM processes data in both directions (past to future and future to past). This allows the model to have a better understanding of the context by considering both past and future information.

The researchers prepared a dataset by combining weather parameters and solar generation data and then enriched the dataset by bringing in meteorological data as well as physical characteristics of the solar panels deployed in the respective solar plants. The BI-LSTM model, they say, can predict the future solar power generation of a specific solar plant on both short and long horizons regardless of the geographical position of the solar plant.

“For short-term prediction, we can predict the generation of solar power for fifteen minutes to one hour ahead, and for long-term forecasting, we are able to predict PV power generation for 1-3 days ahead with noticeable accuracy,” the paper says.

Mondal, Roy and Giri compared the results of the proposed model with the existing dataset and multiple standard deep learning models and found that “our model produced better performance than traditional models.” They also validated their model using different solar plants in Durgapur, India. “For long-term forecasting, our model also outperformed the base model.”

From data to decisions

In an emailed response to quantum’s questions, Dr Giri said the researchers used a time series dataset containing 14 independent features and one dependent feature. The dataset contained data for every 15 minutes from January 1 to December 31, 2022. “We tested our trained model with other datasets collected from solar plants situated in Durgapur, West Bengal. Then we tested our model with a published dataset collected from Denmark. We found our model gives similar results.”

No model is flawless. “We faced some limitations during the test,” Dr Giri said, noting that when there were abrupt changes in the weather parameters, they got slightly different results.

Asked if the ensemble model would call for high computational power, Dr Giri said that their model “is quite light weight” containing only 1.2 million parameters. “We believe that it will not be an issue during large-scale implementation,” he said.

“We believe that our model is trained with a very small amount of data,” he said, adding that they were trying to extend our work with a large amount of data to improve the efficiency of our model.

This work will help the researchers explore the other dimensions rather than a specific dataset but also the scientific knowledge of specific domains, Dr Giri told quantum.