Articles | Volume 7, issue 12
Atmos. Meas. Tech., 7, 4387–4399, 2014
Atmos. Meas. Tech., 7, 4387–4399, 2014

Research article 11 Dec 2014

Research article | 11 Dec 2014

Regression models tolerant to massively missing data: a case study in solar-radiation nowcasting

I. Žliobaitė et al.

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Cited articles

Aggarwal, Ch. (Ed.): Data Streams – Models and Algorithms, Springer, 2007.
Allison, P.: Missing Data, Sage Publications, 2001.
Babcock, B., Babu, S., Datar, M., Motwani, R., and Widom, J.: Models and Issues in Data Stream Systems, in: Proc. of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS, 1–16, 2002.
Bacher, P., Madsen, H., and Nielsen, H. A.: Online short-term solar power forecasting, Sol. Energy, 83, 1772–1783, 2009.
Bhardwaj, S., Sharma, V., Srivastava, S., Sastry, O., Bandyopadhyay, B., Chandel, S., and Gupta, J.: Estimation of solar radiation using a combination of Hidden Markov model and generalized Fuzzy model, Sol. Energy, 93, 43–54, 2013.
Short summary
We present a case study in solar/radiation nowcasting using environmental sensor measurements as inputs. While some sensor readings may oftentimes be missing, predictions need to be output continuously in near real time. We are after linear regression models that would be robust to missing data, i.e., that would perform well with or without data gaps. We recommend using regularized a PCA regression with our established guidelines for building robust regression models.