Forcasting Solar Irradiance by Learning from Time Series Data

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Course project for CSE 546 (grad-level Machine Learning). In collaboration with Kousuke Ariga.

Power generation from solar panels at a Photovoltaic (PV) power plant is directly proportional to solar irradiance, which depends on weather conditions. Solar irradiance forecast is the pivotal factor for power generation scheduling at photovoltaic (PV) power plants. To this end, we propose a framework to model solar irradiance that leverages both short-term dependency (several days) and dependency on time in the annual cycle. We focus on two classes of methods, autoregressive (AR), and autoregressive with exogenous variables (ARX), and investigate LASSO regression, and non-linear methods, including support vector regression (SVR) and long short-term memory (LSTM) as the underlying mechanism. Our experiments on data collected at a PV power plant for over five years show that considering external information such as weather on top of the historical solar irradiance data for longer period of time lowers the error of solar irradiance forecast.


Originally posted on 12/15/2017. Last updated on 05/20/2023.


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