Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence
• Data Science

Background:

• Renewable energy systems face challenges due to unpredictable power output and fluctuations.
• Current forecasting methods struggle with demand spikes, leading to inefficiencies and instability.
• Accurate power output estimation and distribution management are crucial for widespread renewable energy adoption.

Purpose of the Study:

• To introduce the Probabilistic Systematic Processing Method (PSPM) for improved short-term demand forecasting and power distribution management.
• To enhance the balance between energy generation and distribution states in real-time.
• To dynamically detect and differentiate inappropriate surges in power distribution within renewable energy systems.

Main Methods:

• The Probabilistic Systematic Processing Method (PSPM) utilizes reward-based state model learning.
• It incorporates real-time and historical data, including consumption, peak generation, and disconnections, for proactive demand anticipation.
• Validation was performed using the Smart Grid Data set from ARRA projects.

Main Results:

• PSPM demonstrated a 20% improvement in forecast success rate compared to existing methods.
• Distribution efficiency was increased by 25% through the application of PSPM.
• Analytical latency was reduced by 35%, showcasing enhanced operational speed.

Conclusions:

• PSPM offers a novel approach to improving the resilience and operational efficiency of renewable energy systems.
• The method combines probabilistic analysis with reinforcement learning, addressing a gap in adaptive energy distribution research.
• PSPM is practical, scalable, and has potential applications in sustainable power infrastructure automation, energy policy, and smart grid management.