Use of machine-learning for monitoring solar thermal plants

Abstract

A machine-learning algorithm (MLA) was developed to assess the operational state of solar thermal plants, based on the data of only one temperature sensor, and the irradiance and ambient temperature data from the nearest weather station. A detailed requirements analysis of the situation results in the classification of a multivariate time series problem. Neural networks used in the field of data science are ideally suited for problems of this type. Data from the operational monitoring system, which runs a rule-based algorithm, were used to train the neural network using the software framework TensorFlow. It was shown that the chosen MLA can detect malfunctions such as heat loss due to gravity-driven circulation during night. However, further development towards a practical tool requires not only far more data for training and validation. It became clear that corresponding pressure data are needed to classify temperature transients and to attribute these classes to certain malfunctions.