Adaptive Renewable Energy Forecasting Utilizing a Data-Driven PCA-Transformer Architecture

Abstract

The incorporation of renewable energy sources into the power grid has necessitated the development of sophisticated forecasting models that can effectively handle the inherent fluctuation and uncertainty associated with renewable energy generation. In this study, an adaptive principal component analysis (PCA)-enhanced transformer architecture, hereinafter referred to as PCA-Transformer, is developed to enhance the performance of transformer models in predicting renewable energy output. The proposed model uses PCA to dynamically determine and adapt the transformer architecture and prioritize the most informative features from time series data, thereby improving the model’s attention on relevant information and reducing computational burden. This is essential for accurately capturing the intricate temporal patterns and nonlinear relationships that are typical present in renewable energy time series data. The PCA-Transformer enhances the performance of transformer models in sequence-to-sequence predictions by incorporating an adaptive mechanism that customizes their structure based on the best PCA eigenvectors. The architecture adaptively aligns with the underlying patterns in data by adjusting the number of attention heads and critical dimensions within each transformer block. The adaptability of the proposed architecture is crucial for effectively simulating the complex nature of renewable energy generation patterns. The efficiency of the proposed model was evaluated using the Alice Springs Australia DKASC-ASA and EIA Energy datasets. The proposed model has superior forecasting performance than traditional transformer models and cutting-edge renewable energy forecasting methodologies.

Keywords

• AutoML
• principal component analysis
• renewable energy forecasting
• time series analysis
• transformer