In the future there will be an increased uptake of solar and battery systems in the residential sector, driven by falling battery costs and increasing electricity tariffs. The increased uptake means we need new methods to forecast electricity demand when considering these technologies. This paper has achieved this goal using a two stage model.
Stage 1: A machine learning demand model has been created applying adaptive boost to a regression tree algorithm, achieving an RMS error of 0.25. The model has been used to simulate the individual base-demand for 50 homes in a precinct.
Stage 2: A linear programing model has been developed that determines the impact of solar and battery storage on that base demand, and optimizes the system capacities for each home in the precinct while limiting emissions. This model shows reducing emissions by 50% through solar and battery storage cost 2.6% more than the grid only scenario.
Read the paper HERE.