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Climate Impact on Grids: ANN & Regression Models for Energy-Efficient Solutions

thermal analysis

Increase in demand in selected planning regions (a) 2050, RCP 45 (b) 2050, RCP 85 (c) 2080, RCP 45 (d) 2080, RCP 85

Background:

Climate change is significantly impacting global energy systems, especially in regions with weak transmission grids. This study explores how artificial neural networks (ANN) and regression models can be leveraged to assess the impact of climate scenarios on electricity demand and infrastructure performance. By using energy forecasting techniques and analyzing long-term climate data, this research provides insights into how future climate variability might affect grid stability and energy planning. Additionally, it highlights the role of energy-efficient solutions in adapting to climate-induced challenges, ensuring that energy systems remain sustainable and resilient in the face of changing environmental conditions.

Solution:

Our energy scientists developed an ANN and regression-based framework to quantify climate impacts and offer actionable insights:

1. Demand Forecasting:

ANN and Quadratic Regression models analyzed historical demand patterns and climate projections, achieving forecasting efficiencies of 92.42% (PESCO), 92.02% (LESCO), and 91.98% (IESCO). These models projected significant demand increases in hotter regions, emphasizing the need for targeted planning.

2. Transmission Line Thermal Analysis:

Thermal modeling revealed a potential 23.34% reduction in transmission capacity due to rising temperatures. This highlights the necessity of upgrading transmission infrastructure with temperature-resistant cables.

3. Renewable Energy Integration:

Comparative analysis of thermal and solar PV systems underscored the resilience of renewables, advocating their increased adoption to meet energy efficiency and climate goals (aligned with SDG-7 and SDG-13).

4. Climate Scenarios and Mitigation:

Scenario analysis explored optimistic (RCP 45) and worst-case climate scenarios, recommending smart grid technologies, energy-efficient solutions, and infrastructure improvements to mitigate adverse impacts.



Results:

  • Demand Projections: Sharp increases in peak demand, particularly in hotter regions.
  • Transmission Impact: Up to 23.34% capacity loss in existing infrastructure under extreme conditions.
  • Model Performance: Robust forecasting models with over 92% accuracy in key regions.
  • Policy Recommendations: Includes renewable integration, smart grid technologies, and enhanced transmission systems.

This research demonstrates how advanced analytics can assess and address climate-related challenges, ensuring resilient and sustainable energy systems in developing nations. Here are some of the Energy-Efficient Solutions developed by our team.

Energy-Efficient Solutions

Distribution of percentage Change in Ampacities for 500 kV and 220 kV Transmission Lines.

energy efficient solutions

Percentage decrease in overall thermal power output across the years under RCP 45 and RCP 85

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