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Evidence of Analytical and Research Skills

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Evidence of Analytical and Research Skills

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Ai for forecasting and monitoring reducing energy consumption of HVAC system

1. PlantPro:

1. PlantPRO, an AI-driven energy optimization system, was implemented to enhance the performance of HVAC systems, which typically account for over 40% of a building's energy consumption. Here’s a summary of how PlantPRO reduced and optimized energy consumption:

2. AI and Machine Learning Integration:

PlantPRO uses AI analytics and machine learning to monitor and optimize HVAC systems, acting as a "virtual engineer" that makes real-time adjustments to system settings, ensuring optimal operation.

3. Energy Savings:

By implementing PlantPRO, YdotX observed over an 8% reduction in electricity consumption within the first month without compromising the standard of living for residents.

4. Fault Detection and Diagnosis:

The system provides real-time automatic fault detection and diagnosis, reducing the need for manual monitoring and minimizing system downtime.

5. Remote Monitoring and Data Access:

All recorded and real-time data can be accessed remotely, allowing for easier sustainability reporting and evaluation.

6. Operational Efficiency:

PlantPRO minimizes the need for specialists to maintain the system, thus lowering operational costs while improving the overall energy efficiency of the HVAC systems.

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Reduce energy consumption of HVAC systems using Machine Learning

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Evidence of Strategic Thinking and Solution Design

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Evidence of Strategic Thinking and Solution Design

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1. MLP Regressor and Transformer Models for Energy Forecasting

Application: Use MLP Regressors and Transformer models to predict energy consumption, factoring in variables like time-of-day, weather, and campus occupancy. Transformers are particularly effective for long-range dependencies, making them suitable for capturing seasonal energy patterns.

Enhancement: Hyperparameter tuning (e.g., learning rate, number of hidden layers) will optimize MLP and Transformer models to improve forecast accuracy and reduce overfitting.

2. Generative Adversarial Networks (GANs) for Data Simulation and Scenario Planning

Application: GANs can simulate energy usage scenarios based on various occupancy and environmental factors, supporting scenario-based planning for future energy needs.

Enhancement: GANs can provide realistic simulations to test “what-if” scenarios, allowing La Trobe to plan energy distribution under different conditions and reduce reliance on grid electricity.

3. Explainable AI (XAI) Tools for Interpretability

Application: Use SHAP and LIME to provide transparency in AI-driven recommendations for actions like adjusting HVAC settings or switching energy sources.

Enhancement: XAI helps facilities managers understand the “why” behind AI-driven decisions, ensuring they can confidently implement recommendations and optimize system settings.

4. Causal AI for Targeted Intervention Recommendations

Application: Causal AI models identify the most influential variables (e.g., temperature, occupancy, lighting) impacting energy efficiency, allowing focused interventions.

Enhancement: By analyzing causal relationships, La Trobe can make targeted improvements, such as adjusting insulation or optimizing HVAC operations for specific buildings.

5. Reinforcement Learning for Real-Time Energy Source Switching

Application: Reinforcement learning algorithms can dynamically choose between solar, grid, or natural gas energy sources based on real-time factors (e.g., grid demand, solar availability).

Enhancement: With reinforcement learning, the system learns optimal source combinations, reducing costs and emissions by prioritizing renewable energy sources whenever possible.

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Evidence of Communication and Collaboration Skills

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Evidence of Communication and Collaboration Skills

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This Padlet showcases examples of communication and collaboration skills developed through teamwork and stakeholder engagement during the AI Energy Optimization project. Each piece highlights the ways in which I facilitated collaboration and presented key findings to drive project progress and ensure alignment with La Trobe University’s sustainability goals.

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LTU3IND - Communication and Collaboration.docx