In order to support a substantial reduction in Ireland’s greenhouse gas emissions and make Ireland more energy independent and energy resilient, the Irish Government’s Climate Action Plan (CAP2021) has established an 80% renewable energy target for the electricity sector by 2030. The proposed pathway includes a more rapid build-out of renewable generation capacity from wind and solar resources, as well as behavioural changes in household electricity usage patterns.
However, there is limited information on the electricity generation profile from solar for individual households – solar irradiance maps from mathematical models are presented at a monthly level, inhibiting the development of predictive models at fine spatio-temporal scales which require real-time data. At a grid management level, it is not possible to estimate the contribution of residential solar to electricity generation, leading to observable spikes in electricity generation during low energy usage periods nationally. It also results in households spending more on electricity than they need to, if they optimised their usage of their own micro-generated electricity.
Here we present a proof of concept spatio-temporal model for solar irradiance in real-time at the eircode level of spatial resolution. This model is based on acquired data from citizen science contributors and the predictive models allow them to schedule their electricity usage to correspond with times of maximum micro-generation of electricity, ultimately reducing demand on the national grid and reliance on fossil-fuel based electricity generation methods.
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