Cost-Effective Processes of Solar District Heating System Based on Optimal Artificial Neural Network

摘要

Aligning with the EU 2030 climate and energy package to achieve a share of at least 27% of renewable energies,and to improve the energy efficiency by at least 27%,the future solar district heating systems(SDHS)may enable the transition to a complete renewable society.Even though this promising tendency of the SDHS,a range of potential barriers are obstructing the wide deployment of SDHS and promoting high variation in quantifying the SDHS benefits over its lifetime.In this context,the optimization approaches are a viable option for determining the optimal structure,sizing,and operation of the SDHS.However,Meta-heuristics optimization models are computationally very expensive and have many limitations regarding the optimization process.Aligning with these challenges,this work tends to develop a robust Artificial Neural Network model based on Bayesian Optimization to solve the computational obstacle associated with heuristics optimization models for SDHS.