Informed Feature Selection for Data Clustering of CSP Plant Production

Abstract

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 S_i, can be used as a supplemental feature to group like days. A framework for creating and training a deep neural network to predict S_i 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 S_i from the neural network, S_i from the full annual simulation, or no S_i. Ultimately, the results suggest that using S_i as an additional clustering feature improves the data clustering simulation accuracy by 1.4%.