Clir Renewables has released new technology to model the variability of hourly energy production. Powered by machine learning and over 200GW of data, the software improves upon the accuracy of industry standard approaches to modelling production shape and volume. The result is quality data to support owners and operators with production and revenue forecasting to optimize offtake strategy. Clir’s data scientists have overcome many of the pitfalls of standard statistical models. A machine learning model is trained using hourly gross energy from the wind or solar farm and climate data from the corresponding period. The projections from the trained model are then tested against actual production in the years following the training data period. Once trained, the model is used to produce a time-series distribution based on more than twenty years of data. The result is a simulation of hourly gross energy production uncertainty bands in any month, quarter, or year based on the historical trend.  

Clir Renewables | www.clir.eco