Conv-Ensemble for Solar Power Prediction With First Nations Seasonal Information

Power generation forecasting, especially for solar power, is crucial for future energy planning. In this study, a novel framework, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FNS-Metrics</i>, is proposed to integrate seasonal information from First Nations calendars into solar power forecasting. Furthermore, a novel Conv-Ensemble framework is proposed, leveraging the high-level feature extraction capabilities of Conv1D layers along with the low-level feature extraction abilities of transformer and LSTM networks. A weighted feature concatenation technique is also integrated into the proposed approach to combine the features effectively. To validate the proposed FNS-Metrics and Conv-Ensemble framework, a new dataset is constructed by collecting power and weather data from the Desert Knowledge Australia Solar Center in Alice Springs and integrating data related to First Nations seasonal cycles. Experiments on this dataset show that the Conv-Ensemble framework with FNS-Metrics outperforms traditional approaches, achieving state-of-the-art solar power prediction with the highest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.8641 and the lowest MSE of 22.41. These represent a 14.60% and 26.21% increase compared to the baseline configuration of Conv-Transformer. The ablation study demonstrates that the Conv-Ensemble framework improves performance compared to the baselines. Furthermore, the results for individual and combined FNS-Metrics features show a progressive improvement in performance.