Optimizing Enterprise Renewable Energy Operations Using Bidirectional GRU and Enhanced Scaled SFLA
Abstract
The worldwide movement to integrate renewable energy offers enterprise-level energy systems both operational obstacles and opportunity. Due to the inherent unpredictability and variability of wind and solar power sources, effective energy management requires both precise forecasting and flexible operating techniques. In order to facilitate smooth and intelligent company operation inside renewable-integrated power systems, this research suggests a unique hybrid framework that combines a Bidirectional Gated Recurrent Unit (Bi-GRU) neural network with the Enhanced Scalable Shuffled Frog Leaping Algorithm (ESSFLA). The suggested approach uses historical wind and photovoltaic (PV) generating data to anticipate short-term hybrid renewable energy output using the Bi-GRU model. The model's hyperparameters as well as important operational decision factors including learning rate, temporal window size, and system scheduling techniques are then optimized using the ESSFLA. Experimental validation is conducted using a real-world dataset that includes hourly wind and photovoltaic electricity outputs from northern China. According to the results, the ESSFLA + Bi-GRU model optimizes corporate operational outcomes while achieving improved forecasting accuracy, lowering the Mean Absolute Percentage Error (MAPE) to 9.2% and raising the R2 score to 96%. In particular, the model allows for 18.9% cost reductions, a 29.5% decrease in grid load, and rates of renewable energy use that surpass 81%. These results unequivocally show how the suggested model may enhance decision-making in situations involving the adoption of renewable energy, providing a scalable and effective answer for contemporary energy businesses seeking to accomplish both sustainability and economic efficiency. A potential approach to integrating swarm intelligence with deep learning in upcoming smart grid and energy management applications is the suggested ESSFLA + Bi-GRU architecture.
DOI:
https://doi.org/10.31449/inf.v49i17.9940Downloads
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