Optimizing Lightweight Recurrent Networks for Solar Forecasting in TinyML: Modified Metaheuristics and Legal Implications

The limited nature of fossil resources and their unsustainable characteristics have led to increased interest in renewable sources. However, significant work remains to be carried out to fully integrate these systems into existing power distribution networks, both technically and legally. While reliability holds great potential for improving energy production sustainability, the dependence of solar energy production plants on weather conditions can complicate the realization of consistent production without incurring high storage costs. Therefore, the accurate prediction of solar power production is vital for efficient grid management and energy trading. Machine learning models have emerged as a prospective solution, as they are able to handle immense datasets and model complex patterns within the data. This work explores the use of metaheuristic optimization techniques for optimizing recurrent forecasting models to predict power production from solar substations. Additionally, a modified metaheuristic optimizer is introduced to meet the demanding requirements of optimization. Simulations, along with a rigid comparative analysis with other contemporary metaheuristics, are also conducted on a real-world dataset, with the best models achieving a mean squared error (MSE) of just 0.000935 volts and 0.007011 volts on the two datasets, suggesting viability for real-world usage. The best-performing models are further examined for their applicability in embedded tiny machine learning (TinyML) applications. The discussion provided in this manuscript also includes the legal framework for renewable energy forecasting, its integration, and the policy implications of establishing a decentralized and cost-effective forecasting system.