Abstract
The importance of incorporating underlying theory and domain knowledge while building artificial intelligence-based predictive models is examined. Using the context of predicting intraday solar radiation, we show that a theoretically grounded predictive model yields better performance and offers more interpretability and generalizability than a model that relies solely on other variables. Inclusion of theoretically-guided variables in data-driven predictive models is proposed as a means to mitigate overfitting and reduce potential bias.
Recommended Citation
Papneja, Hashai, "Theory-Guided AI for Intraday Solar Radiation Prediction" (2019). AMCIS 2019 Proceedings. 10.
https://aisel.aisnet.org/amcis2019/green_is_sustain/green_is_sustain/10
Theory-Guided AI for Intraday Solar Radiation Prediction
The importance of incorporating underlying theory and domain knowledge while building artificial intelligence-based predictive models is examined. Using the context of predicting intraday solar radiation, we show that a theoretically grounded predictive model yields better performance and offers more interpretability and generalizability than a model that relies solely on other variables. Inclusion of theoretically-guided variables in data-driven predictive models is proposed as a means to mitigate overfitting and reduce potential bias.