Team D5 : SmartWatt

Risks

1. Need to test how different LP solvers (CVX, PuLP etc) output different results for the optimization and choose which one is the most accurate at reducing energy consumption cost

Changes

Direct communication between the ESP32 and Django backend for storing load consumption data

Schedule & Progress

• Optimization algorithm framework finalized
• Design specifications (including components in the embedded layer, ML model, backend and mobile app and communication protocols between these finalized)
• Design presentation done
• external API libraries and datasets (and data collection mechanisms for ML models) identified
• Sketched up a model of the demo house with dimensions and ordered materials accordingly.
  
  Additional Weekly Questions (A written by anyab, B written by Erika , C written by Maya)
• Part A :
• Health: SmartWatt contributes to community health by reducing reliance on non-renewable energy sources (increasing reliance on solar panel energy, which will help decrease air pollution in the long run through all of the fossil fuels that haven’t burnt). Additionally, SmartWatt has an optimization function that maximizes consumption of solar power, thereby reducing the need for drawing power off the grid
• Welfare: By optimizing energy costs and enhancing efficiency, SmartWatt supports economic welfare, reducing household energy expenses and ensuring consistent power availability for essential needs.
• Safety : Users can see what their power consumption for different appliances are, and can determine if an appliance is faulty based off the power profile.
• B : SmartWatt, our household energy optimization and monitoring system, addresses the growing need for sustainable energy management within residential communities. Rising energy costs and increasing awareness of climate change have created a strong societal demand for solutions that reduce consumption, lower utility bills, and promote environmentally responsible behavior. SmartWatt empowers homeowners to monitor and optimize their energy use through real-time analytics, personalized recommendations, and automated control of appliances. By reducing energy waste, SmartWatt not only decreases household expenses but also contributes to broader efforts to lower carbon emissions and combat climate change—issues that affect society as a whole.Socially, SmartWatt fosters a culture of energy-conscious living within neighborhoods and communities. The platform can be extended to include social features that allow users to compare their energy savings with neighbors, participate in local energy-saving challenges, and share tips, encouraging collective action toward sustainability. Additionally, SmartWatt’s affordability and user-friendly interface ensure accessibility for diverse socioeconomic groups, helping low-income households reduce utility costs and alleviate energy poverty. By promoting sustainable habits and supporting energy equity, SmartWatt advances social and environmental responsibility, creating a positive impact on both individuals and their communities

C: Consideration of Economic Factors

SmartWatt is designed to optimize energy consumption in residential homes, directly impacting economic factors by reducing household electricity costs. By integrating real-time monitoring, predictive analytics, and automation, SmartWatt ensures that homeowners can minimize unnecessary energy expenditure, ultimately leading to lower utility bills. The optimization algorithms leverage data from smart sensors to shift power usage to off-peak hours, allowing users to take advantage of lower electricity rates where time-of-use pricing is available.

On a broader scale, SmartWatt contributes to economic efficiency by reducing strain on power grids, decreasing the demand for expensive energy production during peak times. The system also enables better load balancing, which can prevent overuse and extend the lifespan of household appliances, reducing replacement and maintenance costs over time. By making energy management more accessible and cost-effective, SmartWatt supports economic sustainability and affordability, ensuring long-term financial benefits for users while promoting responsible energy consumption at a larger scale.

However, the initial cost may be high as many appliances will need to be wired into Home Assistant for both control and monitoring. With stuff like the ESPHome it is a lot cheaper given some technical knowledge

Presented the proposal to the class

Risks : 

1. Right now, our load forecasting model is trained on synthetic data. Main risk is overfitting to synthetic data. Once we switch to real-time power consumption logs from ESP32 sensors, our model may struggle to adapt to real-world variations such as sudden spikes in energy usage (a heater turning on) or unusual load drops (someone turning off all appliances).

Mitigation : Collect real IoT data logs from ESPHome devices to improve model accuracy. Implement outlier detection to ignore unusual spikes or drops in power usage. Use XGBoost & RandomForest models, which handle nonlinear patterns better. Will plan to use multiple forecasting models (XGBoost, ARIMA, LSTMs) and use ensemble methods to combine predictions.

2. The reliability and quality of the optimization algorithms are directly dependent on the quality of the forecast data (for load forecasting + solar power + grid prices). Lower forecast errors lead to more accurate optimization results, whereas high forecast errors can lead to bad optimization outcomes

Changes : 

No significant design changes have been made to the system yet.

Progress:

• Solcast API integration for access to real-time weather forecasts
• Load forecasting module that predicts load for next 24h within household
• Set up home assistant instance
• Connect ESPHome to home assistant
• We have researched and validated the necessary electronics components to execute our plan