Deep Learning & Multi-Criteria Decision-Making for Sustainable Urban Energy
A Dynamic Decision-Making Framework for Prioritizing Renewable Energy Technologies in Smart Cities Using Deep Learning and Hybrid Multi-Criteria Decision-Making
This research introduces a novel, adaptive decision-support framework that integrates Long Short-Term Memory (LSTM)-based short-term renewable energy forecasting with an interval-valued Pythagorean fuzzy Best-Worst Method–TOPSIS (IVPF-BWM–TOPSIS). This enables forecast-driven and temporally adaptive prioritization of urban energy technologies, moving beyond static expert-based evaluations. The framework assesses five green energy options: rooftop solar, wind energy, smart grids, solar-integrated electric vehicle infrastructure, and battery energy storage. It utilizes criteria based on forecasted technical feasibility and scalability. Rooftop solar achieved the highest score (RDC = 0.65), followed by solar-integrated EV infrastructure (RDC = 0.566) and smart grids (RDC = 0.55). Wind energy ranked lowest due to limited urban utility. Sensitivity analysis and stress tests confirm the framework's robustness, demonstrating its utility for data-driven, adaptable renewable energy planning in smart cities.
Executive Impact
Our AI-powered analysis reveals the critical metrics and projected outcomes for your enterprise.
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RDC for Rooftop Solar
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Weight Change Tolerance (Rank Stability)
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Stress Scenarios Tested
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Days Solar & Wind Forecast Horizon
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Framework Overview
This section details the integrated methodology combining advanced AI forecasting with fuzzy multi-criteria decision-making. It highlights how the framework transcends traditional static evaluations by adapting to real-time environmental conditions and stakeholder preferences.
Enterprise Process Flow
Define Criteria and Alternatives
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Collect data (Solar irradiance, wind speed from Solcast and expert survey)
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Forecast trends using LSTM
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Adjust ratings based on forecasted solar irradiance and wind speed
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Apply IVPF-BWM for Criteria Weighting
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Construct Hybrid Decision Matrix
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Rank Alternatives using IVPF-TOPSIS
| Feature | LSTM-Fuzzy Framework | Traditional MCDM |
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| Data-Driven Insights |
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| Scalability & Resilience |
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Key Findings & Prioritization
This section presents the core results of the framework's application, highlighting the prioritization of renewable energy technologies and the factors influencing their rankings. It emphasizes the data-driven insights derived from the integrated AI and fuzzy logic models.
0.65
RDC Score for Rooftop Solar (A1)
0.567
RDC Score for Solar-Integrated EV (A4)
0.468
RDC Score for Wind Energy (A2)
Robustness and Sensitivity Analysis
This section evaluates the stability and reliability of the proposed framework under varying conditions. It demonstrates that the rankings remain consistent even with significant changes in criteria weights or under different policy-driven scenarios, affirming the model's practical utility.
±20%
Criterion Weight Variation without Rank Reversal
Unwavering Top Performance of Rooftop Solar
Even under extreme policy shifts or specific criteria prioritization, Rooftop Solar (A1) proved to be the most stable and preferred option. This confirms its strong foundational advantages in urban environments where solar resources are abundant and integration is feasible.
Key Findings:
- Rooftop Solar (A1) consistently ranked first across all 15 stress scenarios.
- Solar-EV Charging (A4) and Smart Grids (A3) showed moderate, predictable changes in rank.
- Wind Energy (A2) and BESS (A5) remained lower but stable in ranking, indicating their performance is less affected by varying assumptions.
- The hybrid IVPF-BWM–TOPSIS model provides consistent conclusions irrespective of changing priorities, making it reliable for real-world energy planning.
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Projected Annual Savings
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Annual Hours Reclaimed
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