Improving solar power forecasting with an ensemble of deep learning models

Machine Learning

Artificial intelligence helps predict power generation from volatile renewable energy sources like wind and solar. Machine learning and (a subset of) deep learning are beginning to replace traditional weather and satellite data-based forecasting and statistical prediction models.

Deep learning models, ranging from simple artificial neural networks to complex “long short-term memories” (LSTMs, an architecture that is particularly effective at making predictions based on continuous data), have been introduced to improve the accuracy of predictions.

Now, three researchers from the Indian Institute of Technology, Kolkata — Rakesh Mondal, Surajit Kumar Roy, and Chandan Giri — have come up with an improved AI technique for forecasting solar power. Instead of using a simple deep learning model, these scientists employed an ensemble of deep learning models that “go a step further than simple deep learning models,” which they say has resulted in improved accuracy.

The benefits of AI

Ensemble models, which combine predictions from multiple individual deep learning models, aren't entirely new. In their Energy paper, the authors acknowledge that other researchers have tried ensemble modeling approaches, but say their work “incorporated features that improve the accuracy of predictions.” These features include parameters such as the physical characteristics of a solar panel, such as the number of cells in the panel, the panel's maximum operating temperature, material type, and ambient temperature. “No existing methodology takes these parameters into account when forecasting solar power production,” the authors say.

Mondal, Roy and Giri used a technique called “bidirectional long short-term memory,” or BI-LSTM, which is a type of recurrent neural network (RNN) designed to process continuous data. Unlike standard LSTMs, which process data in one direction (from past to future), BI-LSTMs process data in both directions (from past to future and future to past). This allows the model to take into account both past and future information to better understand the context.

The researchers prepared the dataset by combining meteorological parameters and solar power generation data, and enriched the dataset with meteorological data and the physical characteristics of the solar panels installed at each solar power plant. According to the researchers, the BI-LSTM model is able to predict the future solar power generation of a particular solar power plant in both the short and long term, regardless of the geographic location of the solar power plant.

“Short-term forecasts allow us to predict solar power generation 15 minutes to one hour in advance, while long-term forecasts allow us to predict solar power generation 1-3 days in advance with remarkable accuracy,” the paper states.

Mondal, Roy and Giri compared the results of their proposed model with existing datasets and multiple standard deep learning models and found that “our model performed better than the conventional models.” They also validated their model using various solar power plants in Durgapur, India. “Our model also outperformed the base models in long-term predictions.”

From data to decisions

In response to email Quantum 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 the trained model on another dataset collected from a solar power plant in Durgapur, West Bengal. We then tested the model on a public dataset collected from Denmark. We found that our model gave similar results.”

No model is perfect, and “we faced some limitations during testing,” Dr Giri said, noting that rapid changes in weather parameters gave slightly different results.

When asked whether the ensemble model would require a lot of computing power, Dr Giri said the model is “very lightweight” with just 1.2 million parameters. “We don't think this will be an issue for large-scale implementation,” he said.

“We believe that our models are trained on a very small amount of data,” he said, adding that they are looking to extend their research with larger amounts of data to improve the efficiency of the models.

This work will help researchers explore other dimensions of scientific knowledge in a particular area, not just a particular data set, Dr Giri said. quantum.

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