Leveraging AI and geothermal cogeneration to boost energy efficiency in a multipurpose zero energy building

Abstract

Recent studies have targeted the emission of less CO₂ alongside saving more energy. To achieve part of this purpose, zero energy buildings (ZEBs) are introduced, replacing fossil fuels with renewable energies. This study focuses on a proposed cogeneration geothermal system, comprising two Rankine cycle units and an absorption chiller, designed to fulfill the clean energy requirements of a 5-story ZEB in Birmingham, England. Using BEopt software, the simulation, and optimization of the complex was performed. The net energy consumption of the building was calculated in one year. The energy assessment revealed the building’s electricity consumption at 2594.48 MWh, heating demand at 3157.63 MWh, and cooling requirement at 75.56 MWh annually. Using EES software, the complex’s energy supply system, was analyzed. Exergy efficiency (EE), and cost rate (CR), the outputs of EES, ought to be optimized via a combination of ANN and NSGA-II (Non-dominated Sorting Genetic Algorithm II). The optimization results showed that in the most optimal operating mode, an EE of 69.11 % and a CR of 23.1 $/h can be reached. Evaluating the cogeneration system’s performance in Birmingham demonstrated its capability to generate 6884.61 MWh of electricity, 27841.66 MWh of heating, and 2462.53 MWh of cooling per year using geothermal energy. Comparing the building’s energy consumption with the system’s production highlighted significant savings: 4290.28 MWh of electricity, 24682.71 MWh of heating, and 2385.01 MWh of cooling annually while meeting the complex’s energy needs yearly.