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

Hammad Alnuman1, Ghulam Abbas2, Amr Yousef3,4

  • 1Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia. hhalnuman@ju.edu.sa.

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|July 27, 2025
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Summary
This summary is machine-generated.

The Probabilistic Systematic Processing Method (PSPM) enhances renewable energy forecasting and distribution. This method improves accuracy by 20%, efficiency by 25%, and reduces latency by 35% for resilient energy systems.

Keywords:
Data analysisForward recurring processPeak generationPower distributionRenewable energyState learning

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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.