Artificial neural Network-Based LCOH estimation for concentrated solar power plants for industrial process heating applications

Abstract

A study was condacted to assess the feasibility of a 5MWt solar-only parabolic trough concentrated solar power (CSP) for industrial process heat (IPH) in various locations across Morocco. Furthermore, the initial plant design has been optimized by considering the number of full-load storage hours and the size of the solar field. Additionally, this study employs a Multi-Layer Perceptron (MLP) model, featuring five input parameters(storage hours, solar multiples,Direct Normal Irradiation, latitude, and longitude), two hidden layers, and one output parameter(levelized cost of heat (LCOH)). The model has been trained using Levenberg-Marquardt (LM) training algorithms. According to the study, the Errachidia site was found to be the most efficient location, generating 21983.56 MWhth at a LCOH of 3.31 ¢/kWth for the initial design. The optimal plant design was determined to have a solar multiple of 1.5 across all investigated sites. The statistical analysis revealed that the MLP model proposed in the study, which had 43 neurons in the hidden layer, demonstrates the lowest errors, including the root mean square error (0.0071) and the coefficient of variance (0.1852), and achieved the highest coefficient of determination value (0.99999) during the training process. Furthermore, the disparities in LCOH values between simulated data and the proposed MLP model across six different Moroccan cities, with errors ranging from 0.01% to −4.07%, confirm the reliability of the MLP model for predicting LCOH.