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Using solar power in the process industry can reduce greenhouse gas emissionsand make the production process more sustainable. However, the intermittentnature of solar power renders its usage challenging. Building a model topredict photovoltaic (PV) power generation allows decision-makers to hedgeenergy shortages and further design proper operations. The solar power outputis time-series data dependent on many factors, such as irradiance and weather.A machine learning framework for 1-hour ahead solar power prediction isdeveloped in this paper based on the historical data. Our method extends theinput dataset into higher dimensional Chebyshev polynomial space. Then, afeature selection scheme is developed with constrained linear regression toconstruct the predictor for different weather types. Several tests show thatthe proposed approach yields lower mean squared error than classical machinelearning methods, such as support vector machine (SVM), random forest (RF), andgradient boosting decision tree (GBDT).

Feature Construction and Selection for PV Solar Power Modeling