Solar energy in the classroom: Seasonal analysis of a PV performance dataset from an off-grid educational facility

A seasonal analysis of a long-term dataset produced by an off-grid classroom facility showcasing several solar orientated renewable technologies including building integrated PV and battery storage is presented. The classroom is in frequent use as a lecture theatre, allowing characterisation of user impact on system performance based on a building being used as its primary design function. Located on the western coast of the U.K, the building experiences a typical oceanic climate characterised by a relatively narrow annual temperature range and a high level of precipitation – an environment considered sub-optimal for PV performance. Though greater levels of PV generation are observed during periods of clear skies in the summer months, solar generation can fluctuate significantly throughout the year, with much overlap between seasons. On clear days, the battery system reaches capacity quickly and PV power output drops to the base load of the building, whilst on days with large amounts of cloud PV output can fall below this base load and the batteries slowly discharge. This curtailment of solar generation on clear days highlights the importance of developing control strategies to optimise system performance. Interestingly, the classroom generated almost 1.5 times the electrical demand of a typical U.K family home, demonstrating the potential of solar technology in regions with high levels of cloud cover. Maximising the performance of the building requires accurate methodologies for predicting PV generation and detailed knowledge of building demand profiles. Significant correlation is observed between the solar irradiance, battery state of charge and PV power output, demonstrating the importance of these variables in any solar forecasting model. Demand profiles are deterministic and follow classroom routine. A baseline accounts for persistent systems such as the building management system that are active throughout the day, with demand peaking during occupancy. This information could be incorporated into scheduling algorithms to optimise performance. Consumption is more aligned with the solar generation profile than typical residential buildings that peak in the evening as levels of solar generation fall. The synergistic effect of buildings with different demand profiles could be a mitigation method to minimise the temporal mismatch between generation and consumption.