Informed Feature Selection for Data Clustering of CSP Plant Production




Data Clustering, Machine Learning, System Modeling, Performance Simulation


To make concentrating solar power (CSP) more cost competitive, rigourous optimizations must be run to improve plant design and operations. However, these optimizaitons rely on time consuming annual simulations that solve an electricity dispatch scheduling problem to maximize plant revenue. To reduce the runtime of annual dispatch simulations of CSP plants, a data clustering approach is utilized. This approach assumes that like days of revenue and electricity generation can be identified using weather and price data. Although weather and price are important factors for electricity production, this work investigates how thermal energy storage (TES) inventory at the beginning of a day, denoted as Si, can be used as a supplemental feature to group like days. A framework for creating and training a deep neural network to predict Si is proposed. This model is validated and assessed using eleven sets of testing data that were not used during training. Then, the data clustering approach is performed three seperate times with features of weather and price along with either Si from the neural network, Si from the full annual simulation, or no Si. Ultimately, the results suggest that using Si as an additional clustering feature improves the data clustering simulation accuracy by 1.4%.


Download data is not yet available.


J. Martinek, and Michael J. Wagner. “Efficient Prediction of Concentrating Solar Power Plant Productivity Using Data Clustering.” Solar Energy 224. June (2021): pp. 730–41.

Blair, N., Dobos, A.P., Freeman, J., Neises, T., Wagner, M., Ferguson, T., Gilman, P., Janzou, S., 2014. System Advisor Model, SAM 2014.1.14: General Description. Report. National Renewable Energy Laboratory, NREL/TP-6A20-61019.

Wagner, M.J., 2008. Simulation and predictive performance modeling of utility-scale central receiver system power plants. Thesis. University of Wisconsin-Madison.

Wagner, M.J., Newman, A.M., Hamilton, W.T., Braun, R.J., 2017. Optimized dispatch in a first-principles concentrating solar power production model. Applied Energy 203, 959-971.

Frey, B.J., Dueck, D., 2007. Clustering by passing messages between data points. Science 315, 972-976.

CAISO, 2018. California ISO Open Access Same-time Information System (OASIS). URL:




How to Cite

Tuman, M. J., & Wagner, M. J. (2023). Informed Feature Selection for Data Clustering of CSP Plant Production. SolarPACES Conference Proceedings, 1.

Conference Proceedings Volume


Analysis and Simulation of CSP and Hybridized Systems

Funding data