Interpolating high granularity solar generation and load consumption data using super resolution generative adversarial network

The vast majority of commonly accessible photovoltaics (PV) generation and load consumption datasets have low temporal resolutions, leading to inaccuracies in the modeling and optimisation of PV-integrated battery systems. This study addresses this problem by proposing an interpolation model based on a super resolution generative adversarial network (SRGAN) that generates 5-minute PV and load power data from 30minute/hourly temporal resolutions. The proposed approach is validated by two different datasets including large amounts of residential data and compared to an alternative predictive model. The results indicate that the model can adequately capture the targeted data distributions and temporal characteristics with negligible statistical differences from the measured high resolution data. Moreover, it performs consistently across different types of PV/load profiles and on average it results in 0.32% and 0.28% normalised root mean squared errors (NRMSEs) in daily totals of 5-minute PV and load power values when using hourly data as inputs. Under a time-of-use (ToU) tariff, the interpolated 5-minute data leads to 44.7% and 41.7% error reductions compared to using hourly data for estimating electricity costs and battery saving potentials of a PV battery system. Hence, the proposed model can be potentially applied in a battery sizing tool to obtain more accurate sizing results when only low resolution data is available.

Automated summary

This study introduces an interpolation model based on SRGAN to generate higher resolution PV and load power data from low resolution data, improving accuracy in modeling and optimization of PV-integrated battery systems. Results from validation and comparison show that the model can effectively capture the targeted data features and demonstrate consistency across different scenarios.