Table 8 AI algorithms and future trends for PV and EV optimization by continent.
Continent | Commonly applied AI algorithms | Future trends and implementation potential |
|---|---|---|
Europe | GRU, random forest, hybrid models | Adoption of adaptive algorithms with energy storage integration; strong regulatory support promoting renewable energy optimization and grid stability. |
North America | GRU, LSTM, CNN-based forecasting methods | Increased integration with smart grid systems; emphasis on decentralized control and real-time monitoring for dynamic energy management. |
South America | ANN, LSTM, decision trees | Emphasis on cost-effective AI solutions; gradual implementation driven by local resource optimization and improving infrastructure for renewable energy integration. |
Asia | GRU, Artificial neural networks (ANN), SVM, Ensemble methods | Rapid digitalization in energy management; focus on optimizing high-density urban PV installations and integrated EV charging systems in smart cities. |
Australia | LSTM, GRU, CNN-based approaches | Development of climate-adaptive AI systems to manage fluctuating solar output; focus on robust integration of PV systems with EV charging under variable weather conditions. |
Africa | ANN, Hybrid models, reinforcement learning | Growing interest in AI-driven renewable energy solutions despite infrastructural challenges; future trends indicate increased investment in adaptive, scalable energy management. |
