Tesla solar panel homeowners often face a significant challenge in detecting solar panel performance issues. While solar panel systems, including Tesla, provide data on the energy generated, these insights fail to provide notifications when panels underperform or malfunction. Solar panels performance can be attributed to dirt accumulation, shading, mechanical failures, or electrical issues. Faulty solar panels can remain undetected for extended periods of time, leading to losses in energy generation, and ultimately, financial losses. Early detection of issues can prevent further damage to the solar panel system and optimize the energy output and longevity of the system.

I am proposing a solution that merges detailed environmental data with Tesla solar output metrics. By employing a hybrid machine learning algorithm, we will analyze the actual output and cross-reference them with the theoretical output to identify anomalies in energy generation and expenditure. These anomalies may signal potential malfunctions or inefficiencies in the solar panel system and provide homeowners with real-time data and actionable insights. This allows for prompt maintenance needs that can not only maximize the longevity of solar panels, but also the return on investment of them.

The vision for this product is to empower Tesla solar panel homeowners with a comprehensive monitoring solution that ensures optimal performance and maximizes return on investment. By leveraging advanced machine learning algorithms and real-time data integration, this product will provide diagnostics, real-time alerts, and actionable insights to enable users to detect and address inefficiencies or malfunctions .

The first step will be data acquisition via online API's and personal data. We will develop the model using a time series forecasting and anomaly detection to identify discrepancies that may indicate malfunctions. A user-friendly UI and feedback loop is important to continuously improve the model.

Users would be Tesla solar panel homeowners.

We start by obtaining environmental data through APIs from sources like openweathermap.org, including factors such as solar position, atmospheric conditions, and radiation and use the 'pvlib' Python library to calculate solar irradiance. We compute the expected theoretical output, taking into account the solar irradiance and efficiency rating. The actual solar panel output data from the Tesla app, including metrics like solar energy and grid interactions will need to be cleaned. Various experiments will need to be done to simulate non-functional panel conditions and analyze the financial impact of non-functional panels.

A hybrid machine learning model consisting of LSTM networks for time series forecasting will predict the theoretical output of the solar panel. Additionally, using an isolation forest model will detect any anomalies and a random forest model will help identify and classify any output anomalies. Implementing a feedback loop from user feedback will help enhance the model’s accuracy.

It would be helpful to engage users earlier by conducting user research to refine product features and UI. This would help develop the MVP to tackle user needs. Other models could also be explored, such as unsupervised learning, to help discover hidden patterns in data. It is important to display the anomaly detection in an interpretable manner for users to understand. Integration with other smart home devices or energy management systems can provide a more integrated experience for users. This can also help with increasing the accuracy of the model to understand where the energy is going towards.