A Hybrid Contextual Deep Learning Model to Predict Renewable Energy Generation

Deepak Kanneganti, Sajib Mistry, Sumedha Rajakaruna, Aneesh Krishna, Amin Beheshti

Abstract: Renewable energy sources are widely adopted as they are safer for generating energy with less atmospheric harm. Solar power prediction depends on sunlight to forecast solar power effectively, and one of the significant challenges is weather dependency. Weather conditions, especially during overcast and rainy days, significantly impact power generation and can lead to infrastructure challenges and disruptions in the energy supply. We explore state-of-the-art deep learning algorithms to analyze contextual time series data, crucial for understanding the impact of weather uncertainty on power generation. To address this, we propose a novel deep learning-based hybrid contextual model built using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) networks. This model leverages big data collected over ten years (from 2013 to 2023) to account for the variability and uncertainty inherent in weather conditions. Experimental results demonstrate that the proposed hybrid contextual model exhibits a 4.23% performance improvement over the individual deep learning models in predicting power under weather uncertainty conditions.

External IDs:doi:10.1007/978-981-96-6606-5_17