PV self-consumption: Regression models and data visualization

Sammanfattning: In Sweden the installed capacity of the residential PV systems is increasing every year. The lack of feed-in-tariff-scheme makes the techno-economic optimization of the PV systems mainly based on the self-consumption. The calculation of this parameter involves hourly building loads and hourly PV generation. This data cannot be obtained easily from households. A predictive model based on already available data would be preferred and needed in this case. The already available machine learning models can be suitable and have been tested but the amount of literature in this topic is fairly low. The machine learning models are using a dataset which includes real measurement data of building loads and simulated PV generation data and the calculated self-consumption data based on these two inputs. The simulation of PV generation can be based on Typical Meteorological Year (TMY) weather file or on measured weather data. The TMY file can be generated quicker and more easily, but it is only spatially matched to the building load, while the measured data is matched temporally and spatially. This thesis investigates if the usage of TMY file leads to any major impact on the performance of the regression models by comparing it to the measured weather file model. In this model the buildings are single-family houses from south Sweden region.  The different building types can have different load profiles which can affect the performance of the model. Because of the different load profiles, the effect of using TMY file may have more significant impact. This thesis also compares the impact of the TMY file usage in the case of multifamily houses and also compares the two building types by performance of the machine learning models. The PV and battery prices are decreasing from year to year. The subsidies in Sweden offer a significant tax credit on battery investments with PV systems. This can make the batteries profitable. Lastly this thesis evaluates the performance of the machine learning models after adding the battery to the system for both TMY and measured data. Also, the optimal system is predicted based on the self-consumption, PV generation and battery size.  The models have high accuracy, the random forest model is above 0.9 R2for all cases. The results confirm that using the TMY file only leads to marginal errors, and it can be used for the training of the models. The battery model has promising results with above 0.9 R2 for four models: random forest, k-NN, MLP and polynomial. The prediction of the optimal system model has promising results as well for the polynomial model with 18% error in predicted payback time compared to the reference.