Category: Anya’s Status Reports

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

Accomplishments this week

Home Assistant Automation Implementation [Integration Platform]

Defined shell commands and set up tasks in config yaml files for data publishing via the HA API

Grid Price Integration from Nordpool

• Configured retrieval of real-time electricity prices from Nordpool.
• Integrated Nordpool API into the optimization workflow to factor dynamic pricing.
• Implemented logic to optimize energy consumption based on real-time grid prices.
• Instantiated the grid price optimization problem using plp.LP
• 

Challenges & Next Steps

• Sensor Deployment: I am awaiting final integration of sensors to obtain real-time load consumption data
• Performance Optimization:  I need to fine-tune the grid pricing model for higher accuracy.
• Scalability: Need to expand the system to support multiple optimization strategies dynamically.
• Progress and Schedule
• Completed foundational tasks and progressing into real-time data collection and optimization refinements.
• Minor delays in sensor delivery [due to snow storm] could impact the timeline for model training.
• Action Items for Next Week
  • Deploy sensors and verify real-time data collection.
  • Train models using load consumption data
  • Conduct further tests on the dayahead optimization strategy.
  • Optimize automation workflows in Home Assistant.

[Code can be viewed at : https://github.com/erika-24/smart-watt]

1. Defined system parameters for power optimization (including the decision variables, established constraints for battery and load management, and objective function which is a function of grid power and solar energy produced)
2.  Started working on an optimization class that balances energy production, consumption, and storage.

• Utilized linear programming to optimize deferrable loads.
• Considered cost function variations: profit, cost minimization, and self-consumption.
• Used PuLP LP solver to solve the problem.
• Ran preliminary simulations on sample energy dataFinalized System Components
  • Selected SQLite as the database for storing load consumption and power data, as well as outputs from the ML model
  • Chose appropriate power sensors (INA226) and relay switches, as well as load profiles for the appliances for our model house
  • Defined the embedded layer for system control (including all interactions between RPi5, Django backend, ML model, circuit components)

Completed the block diagrams for the design presentation and went over it as I will be presenting this week

Schedule & Progress

Waiting on Maya to send me real-time power consumption data once the power sensors arrive, so while the load consumption forecast model is almost done, it needs to be trained on real time data. I would say I am on schedule.

Next Steps

• Implement improvements to the LP solver configuration (including implementing the self consumption objective function)
• Begin integration with IoT power monitoring system (research how HTTP requests between ESP32 and the backend occur)
• Conduct further simulations to validate the algorithm’s robustness (on more real-time load consumption data)
• Start working on generating a data frame for grid pricing (via API calls)

 

Work Accomplished  (code available at https://github.com/erika-24/smart-watt)

• Started working on a ML-based time-series forecasting model to predict load demand from training dataset (semi-synthetic for now). Used skforecast models (LinearRegression, ElasticNet, KNeighborsRegressor, RandomForestRegressor, XGBoost).
• Some challenges while writing code : How to handle missing values in data. I used forward fill and backward fill for now, will try using other interpolation methods as well as see what the output looks like if rows with missing data are deleted.Final output: Generated a 48-step ahead forecast (30-minute intervals) for future load demand.

For the photovoltaic power output forecast,

• Integrated Solcast API to fetch solar power forecast data for energy optimization.
• Implemented API calls with rooftop ID and API key authentication.
• Parsed JSON responses and converted them into Pandas DataFrames for easier analysis

I also started doing research on the cost function and objective functions as well as constraints to optimize.  The approach will likely be a combination of linear programming (objective fn is affine) and model predictive control.

Progress :

My progress is on track with regards to the Gantt Chart. I set up synthetic data pipeline for now, but will switch to live IoT device logs.

Things to do for next week:

Current Approach: Using synthetic training data for load forecasting.
Next Step:  Retrieve real-time power consumption logs from IoT/smart meter devices that we plan to use for the demo.
1.  Set up an IoT device integration (ESP32 with a smart plug for getting power consumption data).
2. Use MQTT or ESPHome API to stream real-time power consumption data.
3.  Store device logs in InfluxDB or a time-series database.

Current Approach: Integrated PV output from Solcast, but not yet synchronized with load forecasts.
Next Step: Merge solar and load forecasts to optimize energy storage & grid consumption.

Action Plan:
1. Align solar forecast timestamps with predicted load timestamps.
2. Start implementing the optimization algorithm (Linear Programming, MPC) to balance solar vs. grid power usage

3. Evaluate forecasting accuracy with r2_score and MAE/RMSE

4. Implement hyperparameter tuning for XGBoost to improve accuracy

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