Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Solar Energy Integration in Urban Planning addresses how cities can be designed and retrofitted to harness the sun at scale. You learn to assess urban solar potential using building and geospatial data, plan rooftop and district-scale photovoltaic deployment, and understand how solar integrates with the wider urban energy system, storage and grid. The course connects the technical side to planning realities — zoning, building orientation, shading and policy incentives — and the sustainability goals driving them. You finish able to reason about making an urban area genuinely solar-ready. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers integrating solar energy into urban planning — solar potential assessment, rooftop and district-scale deployment, and designing solar-ready sustainable cities.
📋 Course Objectives
1. Assess urban solar potential from building and geospatial data.
2. Plan rooftop and district-scale PV deployment.
3. Integrate solar with storage and the urban grid.
4. Account for orientation, shading and zoning.
5. Align plans with policy and sustainability goals.
👥 Who Should Enroll?
• Urban planners and architects
• Renewable-energy and smart-city professionals
• Sustainability and policy analysts
• Students of urban energy systems
🚀 Key Learning Outcomes
• The ability to plan urban solar integration.
• A solar-potential or deployment project.
• A systems view of solar-ready cities.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Solar Energy Integration Foundations
Apply linear algebra and calculus principles to optimize solar panel placement in urban environments • Develop mathematical models to simulate solar energy yield and predict energy output in various urban settings • Analyze spatial data to identify optimal locations for solar energy integration in urban planning projects
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines to extract, transform, and load solar energy-related data from various sources • Configure data preprocessing techniques to handle missing values and outliers in solar energy datasets • Evaluate the performance of different data engineering approaches for solar energy integration in urban planning
Model Architecture, Algorithm Design, and Solar Energy Integration Methods
Develop and train machine learning models to predict solar energy yield and optimize energy output in urban environments • Implement algorithmic techniques to integrate solar energy systems into urban planning projects • Optimize model architecture to improve the accuracy of solar energy predictions in various urban settings
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate machine learning models using solar energy datasets and metrics such as mean absolute error and R-squared • Configure hyperparameter optimization techniques to improve the performance of solar energy prediction models • Analyze the results of model evaluation to identify areas for improvement in solar energy integration
Deployment, MLOps, and Production Workflows
Deploy solar energy prediction models in production environments using containerization and orchestration tools • Design and implement MLOps workflows to monitor and maintain solar energy prediction models in production • Configure production workflows to integrate solar energy prediction models with urban planning decision-making processes
Ethics, Bias Mitigation, and Responsible AI Practices
Evaluate the ethical implications of solar energy integration in urban planning and develop strategies to mitigate bias • Develop and implement techniques to ensure fairness and transparency in solar energy prediction models • Analyze the impact of solar energy integration on urban communities and develop strategies to promote responsible AI practices
Industry Integration, Business Applications, and Case Studies
Develop business cases for solar energy integration in urban planning projects and evaluate their feasibility • Analyze industry trends and developments in solar energy integration and their implications for urban planning • Evaluate the effectiveness of solar energy integration in real-world urban planning projects through case studies
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | NumPy |
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