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Anya’s Status Report for 03/15

Work Accomplished:

Spent a significant amount of time debugging the optimization constraints and attempting different techniques to resolve DCP rule violations in the optimization model. To resolve it, I made sure the SOC dynamics equation is now linear in the decision variables, added fallback options (within CVXPy library) to try different solvers if the primary ECOS solver fails.

The SGD and L-BFGS-B methods are somewhat working, for the linear programming solvers its thinking the constraints are of conic form so I need to debug that.

Here is the output for SGD


Started working on a recommendations analyzer that processes energy data to identify patterns and anomalies. I developed a classification system that categorizes insights by priority and potential impact. Added functionality to track user actions on recommendations to improve future suggestions

Worked on the recommendations frontend.


For the frontend, I implemented a responsive dashboard that visualizes energy usage, production, and optimization results across devices

Challenges

One of the main challenges in SGD and LGBFS optimization algorithms is that the forecasted energy demand is very close to the actual load.

  • If the forecasted load closely matches the actual load, then the gradient of the cost function is near-zero.
  • This results in very small updates to optimization variables, reducing the ability of the algorithm to adapt or improve scheduling decisions.
  • Mitigation Technique

Progress

I am on track regards to the schedule. Still waiting on the hardware integration to be complete and polling data from the power sensors, which will be fed into a database and processed to be displayed on the frontend.

Next Steps

  • Power sensor data arrives at high frequency (every few seconds or milliseconds). A proper database in SQLite is required to be created for  logging and retrieval.
  • When calculating average power consumption, factor in transients when devices switch on/off

  • Develop interactive dashboards that display key metrics from the optimization algorithm, such as grid usage, battery state of charge, device schedules, and cost calculations.

  • Start working on the integration of the backend with Home Assistant and ESPHome firmware. I have created the clients and API requests (communication protocol library) but actually need to start debugging
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Erika’s Status Report for 03/15/2025

Accomplishments This Week

This week, I worked on the CAD model for the house, adjusting the design and dimensions to ensure we stay within our material constraints. I refined the layout to optimize material usage. I have also added slots in the back walls to easily and neatly slide in the acrylic sheets and sandwich the wires and electronic components within this two-layered wall.

Additionally, I researched laser cutting techniques and prepared the DXF files, ensuring that the design is ready for an efficient cutting session in the Ideate workspace next week. This preparation included:

  • Finalizing the vector paths to avoid unnecessary cuts.
  • Ensuring the scaling and material thickness were correctly accounted for.
  • Organizing files to minimize waste and speed up fabrication.

I have chosen a low-power approach with multiple passes to minimize the risk of charring the wood. This will hopefully result in a clean, polished look.

Finally, I reached out to IDeATe to request access to the laser cutters. Hopefully, my request will be approved and I can get in there early next week to cut all the basswood sheets needed to assembly the house.

Progress:

Kitchen

  • Floor: 20” x 8”
    • 20” x 12” -> cut off 4 inches from one side
  • Left Wall: 8” x 9 ⅞”
  • Right Wall: 8” x 9 ⅞”
  • Back Wall: 19 ¾” x 9 ⅞” (cut out slots)

Garage

  • Floor: 8” x 8”
  • Left Wall: 8” x 9 ⅞” 
  • Back Wall: 7 ¾” x 9 ⅞” (cut out slots)
  • Roof: 8” x 8”

Bedroom

  • Floor: 12” x 8”
  • Left Wall: 7” (diagonal to 7.09”) x 8”
  • Back Wall: Refer to design -> Max Height: 12”, Width: 11 ⅞”
  • Roof: 9.4” x 8”

Laundry

  • Floor: 8” x 8”
  • Left Wall: 8” x 10”
  • Right Wall: 8” x 7”
  • Back Wall: Refer to design -> Height: 10” to 7”, Width: 7 ¾”
  • Roof: 8” x 12.71”

Full Assembly:

 

Material Usage Planning

Project Schedule Status

My progress is on schedule with our project timeline. The CAD model and laser cutting preparation are key steps before fabrication, and completing them now keeps us aligned with our milestones.

Goals for Next Week

Next week, I plan to:

  • Laser cut the house components and assess fit/assembly.
  • Make any necessary adjustments to the CAD design based on cutting results.
  • Order colored acrylic and create DXF files to laser cut these sheets.
  • Begin assembling the physical prototype to test structural stability.
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Maya’s Status Report for 2025-03-08

  1. Relay ESP32 Setup:
    • Set up a dedicated ESP32 for relay control.
    • Integrated the relay ESP32 with Home Assistant for centralized automation.
    • Began testing the relay functionality with various devices.
  2. Device Integration:
    • Connected minor devices such as a PWM fan, light, and motor.
    • Explored additional devices for potential integration.
    • Evaluated performance and responsiveness of connected devices.
  3. Wiring and Testing:
    • Started wiring and organizing connections for the relay ESP32.
    • Identified and tested different wiring configurations.
    • Determined which setups work best for our needs.
  4. Power Consumption Monitoring:
    • Set up power consumption monitoring to track energy usage.
    • Integrated sensors to measure power draw from connected devices.
    • Began analyzing data to identify potential energy optimization strategies.

Challenges Encountered:

  • Ensuring stable and reliable relay switching for different devices.
  • Managing power distribution effectively to support multiple components.
  • Troubleshooting intermittent connectivity issues between ESP32 and Home Assistant.
  • Calibrating power monitoring sensors for accurate readings.

Next Steps:

  • Expand testing with additional devices to enhance automation capabilities.
  • Optimize wiring layouts for improved reliability and ease of use.
  • Develop automation rules within Home Assistant for better power management.
  • Continue refining the system based on real-world performance observations.
  • Analyze power consumption data to implement energy-saving measures.
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Anya’s Status Report for 03/08/25

Work Accomplished :

  • This week, I focused on developing components of the system that interact with Home Assistant and Nordpool electricity pricing data. The primary tasks included designing and implementing the ESPHomeClient, HomeAssistantClient, and NordpoolClient while also working on the frontend and backend integration.
  • Completed the design report (System Architecture, Quantitative Design Requirements, System Implementation, Testing Methodology)
  • Started working on the frontend + backend functionality of the web app


Implemented an ESPHomeClient to enable communication between ESPHome-based IoT devices and Home Assistant. This module allows devices to send and receive sensor data while maintaining real-time connectivity with Home Assistant.

    • Designed a HomeAssistantClient to interact with Home Assistant’s API, enabling data retrieval and control over smart home devices.

    3. NordpoolClient

    • Built a NordpoolClient to fetch electricity prices from Nordpool via Home Assistant.
    • Integrated API calls to retrieve real-time electricity prices and structured the data for easy analysis.

    On the frontend side, I developed and integrated an energy flow chart that visually represents energy distribution and load optimization within the system.



    Progress:

  • On track with regards to the schedule and GANT chart
  • Currently waiting for integration with Raspberry Pi and  power sensors via ESPHome.
  • Once hardware integration is complete, live data from the sensors will replace simulated inputs in the ML and forecasting module

Tasks to complete next week

  • Conduct end-to-end testing of energy data collection, processing, and visualization.
  • Compare original vs. optimized energy loads to assess efficiency improvements.
  • Identify any bottlenecks in data flow between ESPHome, Home Assistant, and the dashboard.
  • Start integrating predictive analytics for energy consumption forecasting via some sort of inference API
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Erika’s Status Report for 03/08/2025

1. Accomplishments This Week
This week, I focused on two key tasks:

  • Design Review Report: I collaborated with my team to write and refine the design review report, ensuring we clearly outlined our project scope, system architecture, and implementation plan.
  • CAD Model for Demo House: I continued developing the CAD model for our demo house. Specifically, I designed a two-layer wall structure featuring wood on the exterior and acrylic on the interior. This design allows us to sandwich wires and electronic components between the layers for a cleaner, more organized prototype. I also worked on integrating component placements within the model to facilitate efficient wiring and sensor integration.

2. Progress Status
I am on track with my individual tasks for the week. The design review report was completed on schedule, and progress on the CAD model is aligned with our timeline. However, I will need to start laser cutting soon to ensure I do not run into any issues later on.

3. Next Week’s Deliverables
Next week, I plan to:

  • Finalize the CAD of the demo house and determine kerf to verify fit.
  • Laser cut the wood
  • Order the acrylic sheets

This will ensure we stay on track for upcoming milestones and allow us to refine the system before final integration.

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Team’s Status Report for 03/08/25

  • Risk: Hardware Integration Challenges
    • Issue: Ensuring seamless communication between sensors, microcontrollers, and the backend system is critical. Incompatibility between components or unexpected electrical issues could cause delays.
    • Mitigation: We are testing each hardware module separately before full integration and maintaining thorough documentation for troubleshooting.
  • Risk: Energy Monitoring Accuracy
    • Issue: If the system fails to accurately track and optimize energy consumption, the value of SmartWatt is diminished.
    • Mitigation: We are calibrating sensors early in the process, running validation tests, and comparing data against trusted benchmarks..

Contingency Plans:

  • If hardware integration delays occur, we will simulate system behavior in software to continue progress.
  • If energy monitoring accuracy issues arise, we will refine our algorithms and conduct more extensive testing.

2. Design Changes and Their Impact

This week, we made the following adjustments to our system design:

  • Change: Updated Physical Model Design
    • Reason: To improve wiring organization and ease of sensor placement, we modified the demo house’s wall structure to include a layered design (wood exterior, acrylic interior).
    • Costs: This change requires additional material procurement and minor CAD redesign work.
    • Mitigation: We are sourcing materials efficiently and ensuring that fabrication remains within our timeline.

By proactively managing these risks and changes, we aim to keep SmartWatt on track for a successful completion.

ARIMA models from sklearn have been added to the load forecasting module as they have better accuracy on testing data.

Progress

  • Frontend of webapp scoped out
  • ML models working with simulated data
  • API and communication requests between backend and ESPHome and Home Assistant established

Additional Weekly Questions

3. Considerations of Global Factors:

SmartWatt addresses the growing global need for energy efficiency and sustainability by optimizing household energy consumption. As the world transitions to renewable energy, challenges such as grid instability, peak demand management, and high electricity costs are becoming more pressing. SmartWatt provides a data-driven solution by integrating smart meters, IoT-enabled devices, and AI-driven analytics to monitor and optimize energy use in real time. This system empowers homeowners to reduce their carbon footprint, lower electricity bills, and contribute to overall grid stability—an issue that affects both developed and developing regions as energy demands rise.

Beyond local energy concerns, SmartWatt aligns with broader global initiatives such as the United Nations Sustainable Development Goals (SDGs), particularly Goal 7: Affordable and Clean Energy. By enabling users to shift energy consumption to off-peak hours and integrate renewable sources like solar, SmartWatt helps reduce reliance on fossil fuels and supports cleaner energy adoption worldwide. Additionally, in regions where energy access is limited or expensive, SmartWatt’s optimization algorithms can help households maximize their use of available power, ensuring more efficient distribution of resources. As energy prices fluctuate due to geopolitical and economic factors, SmartWatt provides a scalable, intelligent approach to energy management that benefits users across diverse global contexts.

Part B (written by anyab)

Cultural differences influence energy consumption behaviors, regulations, and sustainability priorities. In Europe, where time-of-use tariffs are common, the system automates energy usage during off-peak hours (we use Nordpool API which provides spot electricity prices in Europe) . In regions with flat-rate pricing, it prioritizes renewable energy and battery storage. These region-specific energy insights make the system adaptable to diverse communities, ensuring relevance and effectiveness. Beyond energy consumption habits, cultural attitudes toward technology and automation play a significant role in system adoption. In regions where smart home technology is widely embraced, users may prefer fully automated energy management, while in areas with privacy concerns or skepticism toward automation, the system can offer manual control options to align with user preferences.

Part C (written by sdoshi2)

Environmental factors play a crucial role in the development of SmartWatt.
It is expressly designed to reduce household energy consumption. By optimizing energy usage and promoting demand-side management, SmartWatt helps lower reliance on fossil fuel-based power generation, which remains a major contributor to greenhouse gas emissions. Additionally, by integrating renewable energy sources such as solar panels, the system encourages sustainable energy practices and reduces strain on non-renewable resources.

We also take into account the fact that the users environment will discate the renewable power production and the power consumption based on climate/weather

Beyond energy reduction, SmartWatt also considers energy efficiency in hardware selection. The sensors and microcontrollers used in the system are also chosen to lower power consumption to ensure that the monitoring and optimization processes do not inadvertently contribute to excessive energy use.

Furthermore, SmartWatt aids in environmental conservation by mitigating energy wastage. By identifying inefficiencies such as poorly insulated spaces, users can make informed decisions to enhance their home’s energy efficiency. This proactive approach not only saves money but also reduces unnecessary energy demand, contributing to a broader reduction in environmental degradation. In regions where power generation relies heavily on coal or other environmentally harmful methods, SmartWatt’s ability to shift energy consumption to cleaner energy sources can make a significant difference in reducing ecological impact.

By addressing these environmental factors, SmartWatt aligns with sustainability goals and contributes to a more energy-efficient future, making it an essential tool for environmentally conscious consumers and policymakers alike.