Transfer Learning for Solar Energy Production Forecasting with LSTM: Generalized vs. Specialized Models

I am working on a solar energy production forecasting problem using LSTM multi-step models to predict 1/4/8h ahead of solar energy production for different solar installations. Our goal is to help clients optimize their energy utilization by trading with their neighbours or respective Microgrids. I have clustered households into groups such as small generators, medium generators, and large generators. I am currently developing a multi-household model for each cluster using TensorFlow's LSTM multi-step model tutorial . To improve prediction accuracy and provide a more personalized approach, I would like to explore transfer learning to create specialized single-household models based on the generalized multi-household models. Multi-Household Model (Generalized Model) The dataset consists of 160 time series and includes weather features such as hour, day, month, temperature, DHI, DNI, GHI, precipitation, and solar zenith angle. The model learns from multiple similar households. To better visualize the dataset, here is an example: Hour Day Month TS_0 TS_1 TS_N Temperature DHI DNI GHI Cosine Periodicity Sin Periodicity Other Features 6 1 5 0 0 0 15 … … … … … … 7 1 5 0.1 0.1 0.1 17 … … … … … … 8 1 5 0.2 0.3 0.25 18 … … … … … … 9 1 5 0.5 0.4 0.35 18 … … … … … … 10 1 5 1 0.8 0.85 20 … … … … … … Note: These features related to the weather would be an average of the district that these houses exist in. This current setup utilizes TS_0 to TS_N as examples to learn from each other since…