Maya’s Progress Report for 2025-04-12

Progress This Week:

  1. Ollama Instance Deployment:

    • Set up an Ollama instance running the DeepSeek-R1-8B model for Erika to use.

    • This model supports reasoning which makes it very good for our usecase
    • Deployed it behind the existing reverse proxy with token verification for secure access.

    • We can access it both over the cli and using the api

  2. Python Backend Development:

    • Continued development of the Python backend to add more features and enhance its functionality.

    • Extended API endpoints and improved data handling for integration with Anya’s code and Home Assistant.

  3. Finalized Components
    • Finalized and purchased components needed for the final house, planning to install the moment the house is ready on Erika’s end

 

As the project enters the verification phase, I am planning the following tests to ensure subsystem reliability and accuracy:

  1. Electronics Subsystem Verification:

    • Conduct repeated tests on the power monitoring electronics.

    • Measure the accuracy of power draw readings under various loads (fans, motors, lights, etc.).

    • Check the robustness of the sensor communication and responsiveness of the system to rapid changes in load.

    • Use comparison against known reference measurements (multimeter and calibrated loads) to verify sensor accuracy.

  2. Backend and Infrastructure Verification:

    • Perform functional tests on the backend APIs to ensure correct data logging, error handling, and performance under stress.

    • Test authentication mechanisms, API response times, and data integrity between the Ollama instance, backend, and Home Assistant integration.

    • Simulate prolonged operation to evaluate system stability and resilience.

Maya’s Progress Report 2025-04-05

  1. Python Backend Development:

    • Began development of a new Python backend designed to log data and facilitate smooth communication between Anya’s code and Home Assistant.

    • Established initial framework for data collection and device interaction.

  2. Faculty Check-In:

    • Had a check-in meeting with the project professors to review current progress and gather feedback for the next stages.

  3. Hardware Integration Planning:

    • Developed a plan to integrate smart hardware components into Erika’s model house.

    • Identified key connection points and strategies for aligning physical devices with the model infrastructure.

Erika’s Status Report for 03/29/2025

Progress This Week:

  • Planned the integration of sensors and components into the model house

  • Designed initial wireframes for the user interface, focusing on an intuitive layout that allows users to monitor usage and set preferences for their household energy consumption optimization algorithm.

  • I will be assembling part of the model house tomorrow in preparation for the interim demo.

Challenges:

  • Acrylic sheets took longer than expected to arrive, so I was not able to laser cut them.

  • Balancing UI simplicity with the need to display comprehensive energy data in a user-friendly format.

Next Steps:

  • Finalize sensor placement and integration within the model house after assembly.

  • Continue refining the UI design, focusing on real-time data visualization and interactive elements.

  • Begin initial integration of whole system

Maya’s Progress Report for 2025-03-29

Progress This Week:

  1. Code Improvements and Packaging:
    • Integrated API calls into Anya’s code to monitor and optimize solar energy usage and control actuators
    • Repackaged the updated code as a Docker Image for simple deployment
    • Packed Anyas code as a home assistant addon
  2. Solar Panel Integration:
    • Successfully got the solar panels working and generating power

Team’s Status Report for 03/29

Risks

  • Backend performance under real-time load
    With multiple device schedules and real-time data polling, system lag or crashes may occur if not optimized.
  • Home Assistant integration 
    Integration with HA might face compatibility or API syncing issues depending on setup.
  • Edge-case handling for devices
    Unresponsive or offline devices could cause unexpected failures if not properly handled

Changes

  • System currently loads price data from CSV files, need to change it to connect to a SQLite database, for storing device state, energy usage, and forecasts is in place
  • Add some more loads such as speakers for an audio component in the model house

Progress

  • Frontend and backend frameworks set up
  • User preferences taken into the consideration when performing constrained optimization
  • Model house for demo set up
  • Sensors integrated into Home Assistant

Anya’s Status Report for 03/29

Work Accomplished

  • Implemented conversational AI interface allowing users to query the system about energy optimization at their home.
  • Created suggestion system that guides users with prompt examples for better engagement with the AI assistant
  • The optimization system was enhanced with a user preferences framework that allows the user to select custom timeslots in which to run devices.
  • The system incorporates earliest start times and latest end times for each scheduled device.
  • Then designed a new constraint-based optimization that respects user preferences while maximizing energy savings
  • A three-tier priority system was implemented:
    • Low Priority: Maximizes energy savings with flexible timing (priority weight: 0.3)
    • Medium Priority: Balances energy savings with user-preferred times (priority weight: 0.5)
    • High Priority: Strictly adheres to user time preferences (priority weight: 0.8)
  • Priority settings directly influence how the optimization algorithm weighs time constraints against cost savings

Progress

Frontend and backend up and ready.

Right now, I need to implement the API routes that will actually trigger device control — switching devices on or off based on user input or automated schedules. Slightly behind schedule with regards to the GANT chart.

These routes will act as the bridge between the UI actions and the actuation layer. Once the endpoints are set up and mapped to the appropriate device control logic, the system will be able to execute real actions, completing the loop from user interaction to physical outcome.

Next Steps

  • Tie each route to the code that interacts with the device (ESP 32GPIO pins/ Home Assistant API).
  • Then test with real devices to validate actual switching.
  • Refine ML or linear programming algorithms that decide when to turn devices on/off.
  • Incorporate feedback loops from usage data.

Anya’s Status Report 03/22

Work Accomplished:

  • Did some testing of a LSTM neural network architecture for time-series energy consumption prediction
  • Tracked self-consumption metrics for solar utilization, peak reduction and cost savings % according to a baseline.
  • The original cost is based on the predicted load with original device schedule. The optimized cost uses the new schedules with shifted loads

Mock backend for schedules : (I need to parse the results of optimization algo and figure out whats a good way to put that into device schedules). This is what the frontend should look like

Current display of predicted vs optimzied power. Need to scale this up accordingly once the power sensors are connected to Home Assistant and download the data from there.

Progress: I would say I am a little behind because a) the power sensor data is not logged in, need to do the integration of the backend with Home Assistant to feed that data, look at transience to analyze average power consumption b) The grid pricing data is fed in through Nordpool which has an integration  with Home Assistant, but for now I am training the LSTM by downloading a csv from Home Assistant rather than having it dynamically via an API (API calls are expensive)


Challenges and Next Steps:

  • Improve load shifting algorithm with better device priority handling [figure out a way to parse info about optimized loads from an aggregate to a device level]
  • Also right now, all devices have the same priority.  Need to assign weights based on device priorities (like running a fridge vs fan, fridge is way more important)
  • Use these weights in the objective function to favor high-priority loads when minimizing cost or peak demand.

  • Add detailed logging for performance metrics to validate optimization results

  • Integration between HA and backend would be just creating a docker container and adding that to HA vs API requests

Team’s Status Report for 03/22

Significant Risks & Mitigation Strategies

Potential Risks:

  1. Material Delays: The arrival of the acrylic sheets is critical for completing the assembly of the model house. Any delays could push back integration.

    • Mitigation: The order was placed early this week, about two weeks before the interim demo to allow enough time for laser cutting and assembly.

Contingency Plan: If the acrylic sheets do not arrive on time, we will use tape to secure the wiring and move forward with integrating the electronics.

2.

Risk: Optimization does not accurately reflect the assigned priority levels of individual devices, leading to suboptimal or unfair load scheduling.

Mitigation:

  • Integrate device priority directly into the optimization objective and constraints as weighted penalties or scheduling preferences.

  • Validate post-optimization results by checking if higher-priority devices are scheduled within their preferred windows or receive preferential treatment.

  • Implement unit tests to ensure the priority values are parsed and utilized correctly in the solver.


Design Changes & Justification

  1. Change: Slight modifications to the DXF files were made to improve the fit between the basswood and acrylic layers. I have also decided to not place acrylic sheets over the wooden floors. This reduces materials, lowers cost, and removes unnecessary complexity with laser cutting.
  • Reason for Change: Initial design testing revealed minor misalignments in slots for acrylic insertion. Adjusting the DXF ensures a more precise assembly.

  • Costs Incurred: Additional time spent in CAD revision and verification.

  • Mitigation Strategy: These changes were made early enough, before laser cutting the wood and ordering the acrylic, that they do not impact the schedule. The updated files are ready for cutting as soon as materials arrive.

2. Instead of relying on API requests to interact with Home Assistant, we will use a Dockerized container to deploy and upload the optimized scheduling code directly into the Home Assistant environment. This approach improves modularity, simplifies integration, and allows for more consistent control and updates to the system.

Progress:

  • Testing validation loss and training loss for LSTM for load prediction
  • Frontend developed for device scheduling

Erika’s Status Report for 03/22/2025

Accomplishments This Week

This week, I laser cut all of the basswood sheets for the exterior of the model house. I also designed the DXF files for the acrylic sheets that will form the inner layer of the walls. To ensure timely progress, I placed an order for all required acrylic sheets. Ideally, they arrive in time for the interim demo, but if not we can just tape up the wiring to the wood as a quick solution.

Project Status

Currently, my progress is on schedule. The next steps depend on receiving the acrylic sheets, but designing the DXF files and laser cutting the basswood keeps the timeline on track.

Next Week’s Deliverables

In the upcoming week, I plan to:

  • Laser cut the acrylic sheets once they arrive

  • Assemble the exterior and interior walls of the model house

  • Begin integrating electrical components if time permits

Maya’s Progress Report 2025-03-22

  1. Expanded Device Integration:
    • Successfully got power sensors working for real-time energy monitoring.
    • Integrated motors, PWM fans, and servos for enhanced automation control.
    • Scaled up LED configurations for various smart lighting applications.
  2. Home Assistant Add-on Development:
    • Continued packaging the system as a Home Assistant add-on.
    • Enhanced compatibility for seamless deployment and integration.
    • Refined the Dockerized module for stability and performance.

Challenges Encountered:

  • Ensuring reliable scaling of devices and automation workflows.
  • Addressing minor compatibility issues with different Home Assistant setups.

Next Steps:

  • Finalize and thoroughly test the Home Assistant add-on.
  • Optimize performance for larger-scale automation setups.
  • Continue refining integration with additional smart devices.
  • Document setup and deployment instructions for broader adoption.