Sunny with a Chance of Energy: Predicting the Power of Renewables

The transition to renewable energy is essential for reducing carbon emissions and achieving sustainability. The inherent variability of renewable power sources such as the sun and wind, however, challenges power grid stability and energy planning [1]. Predictive modelling serves to counteract these by providing accurate forecasts of energy production, enabling smooth integration into power grids and optimising energy storage and distribution systems [2]. So, what is the best solution for predicting the power of renewables?

What is the best solution for predicting the power of renewables?

Time Series Analysis and Machine Learning Methods

Models like Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving-Average (SARIMA) analyse past energy data, incorporating seasonal components to identify patterns and forecast short-term energy production [3]. These models are effective; however, they struggle with sudden weather changes.

Machine learning techniques handle complex patterns and nonlinear relationships in data. Neural Networks are useful for capturing time-based patterns in solar and wind energy production. The Support Vector Machines work with complex, high-dimensional and non-linear data to find the data points with the least error or also known as the hyperplane [4]. Gradient Boosting Models (GBMs) combine many simple models such as decision trees (weak learners) to improve prediction accuracy [5]. It sequentially corrects errors made by previous models, focusing on difficult-to-predict data points while weighing predictions to combine the outputs of all models [6].

In summary, combining different models, such as deep learning with statistical methods, improves prediction accuracy.

Challenges in Predictive Modelling

Accurate forecasting requires high-quality weather and energy data [7]. Noisy or missing data can degrade the performance of a model. Energy production depends on the weather, which is unpredictable. Probabilistic forecasting methods help estimate possible variations and improve reliability. Advanced models require powerful computing resources. Efficient model designs and cloud computing help handle large datasets [8]. With greater consumption of renewable energy, predictive analytics will increasingly be used to make it efficient and reliable.

Applications of Predictive Modelling

Forecasting helps balance energy supply and demand, reducing the need for fossil fuel backups. The predictive models guide in improving efficiency and reducing waste. The energy producers use forecasting to make better market decisions, ensuring stable grid operations.

The transition to renewable energy is crucial to a sustainable future, but variability poses challenges to grid stability and planning. Predictive modelling, employing techniques like time series analysis, machine learning, and hybrid models, enhances the accuracy of predictions, enabling better energy storage and distribution. While challenges like data quality and computational demands remain, advances in probabilistic forecasting are enhancing the efficiency of renewable energy integration. As reliance on renewables grows, predictive analytics will play a central role in creating a stable, reliable, and cleaner energy future.

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References

[1] D. S. S. M, R. K. C, K. M, and D. R. A, ‘Enhancing Power Grid Stability with an Advanced Deep Learning Model for Smart Grids’, Int. J. Renew. Energy Res. IJRER, vol. 14, no. 2, Art. no. 2, Jun. 2024.

[2] ‘Predictive Modeling and Forecasting for Renewable Energy: Developing Innovative Data-Driven Models and Machine Learning Techniques’. Accessed: Feb. 14, 2025. [Online]. Available: https://easychair.org/publications/preprint/J7JPn

[3] A. Bajaj, ‘ARIMA & SARIMA: Real-World Time Series Forecasting’, neptune.ai. Accessed: Feb. 14, 2025. [Online]. Available: https://neptune.ai/blog/arima-sarima-real-world-time-series-forecasting-guide

[4] ‘What Is Support Vector Machine? | IBM’. Accessed: Feb. 14, 2025. [Online]. Available: https://www.ibm.com/think/topics/support-vector-machine

[5] N. Aksoy and I. Genc, ‘Predictive models development using gradient boosting based methods for solar power plants’, J. Comput. Sci., vol. 67, p. 101958, Mar. 2023, doi: 10.1016/j.jocs.2023.101958.

[6] ‘A Guide to The Gradient Boosting Algorithm’. Accessed: Feb. 14, 2025. [Online]. Available: https://www.datacamp.com/tutorial/guide-to-the-gradient-boosting-algorithm

[7] M. Fodstad et al., ‘Next frontiers in energy system modelling: A review on challenges and the state of the art’, Renew. Sustain. Energy Rev., vol. 160, p. 112246, May 2022, doi: 10.1016/j.rser.2022.112246.

[8] K. O. Yoro, M. O. Daramola, P. T. Sekoai, U. N. Wilson, and O. Eterigho-Ikelegbe, ‘Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems’, Renew. Sustain. Energy Rev., vol. 150, p. 111506, Oct. 2021, doi: 10.1016/j.rser.2021.111506.https://www.pagerpower.com/wp-content/uploads/2025/02/sunny-thumbnail.png