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