Smart Energy Optimization
AI-powered energy management system optimizing energy consumption through automation and personalized recommendations.
Problem Statement
How might we enable homeowners to reduce energy costs and optimize consumption effortlessly using AI-driven automation and personalized insights?
Problem Background
Since the widespread adoption of smart home devices, homeowners have struggled with manually configuring energy-saving settings, tracking consumption, and optimizing efficiency. Many users find existing solutions cumbersome, non-intuitive, and lacking in proactive recommendations. The need for an automated, AI-powered system is evident to bridge this gap and make energy efficiency seamless.
Research Insights
User Pain Points
- Difficulty in configuring smart home energy settings.
- Lack of personalized energy-saving recommendations.
- Unclear real-time tracking and insights into consumption patterns.
- Inconsistent energy-saving results from existing solutions.
Supporting Data
- 85% of surveyed smart home users expressed dissatisfaction with manual energy settings.
- 100% of participants in our user research were interested in an AI-driven energy management system.
- 66.7% of respondents indicated a need for personalized, behavior-based recommendations.
Feedback
User research validated the need for automation, but users emphasized the importance of transparency and control over AI decisions to build trust.
Landing on the Solution
Based on user pain points, we prioritized the following core features:
✅ AI-driven automated energy optimization.
✅ Personalized recommendations based on real-time data.
✅ Real-time consumption tracking & insights.
✅ Explainable AI (XAI) for transparency in AI decision-making.
Explanation of Solution
After presenting the prototype to users, we discovered that providing clear explanations for AI decisions and offering manual override options increased trust and adoption rates. As a result, we refined the UX to allow greater user control and visibility into AI-driven optimizations.
User Flows & Mockups
- User Flow Diagram
- Lo-Fi Wireframe Diagram
Future Steps
- Refine AI learning models to further improve the accuracy of recommendations.
- Expand integrations with third-party smart home ecosystems.
- Enhance long-term tracking metrics to measure sustained energy savings and behavioural changes.
- Beta test with real users to refine usability and adoption strategies.
Images
Learnings
Product Manager Learnings:
Chidumebi Nkwo
This project reinforced the importance of balancing automation and user control in AI-driven systems. Transparency in AI recommendations is key to adoption. Additionally, user feedback cycles helped refine our approach to delivering trustworthy and effective energy management solutions.
Above all, Co.Lab has been a rewarding experience. I count it a blessing to be part of this cohort session
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.