Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total usage. As result, it is essential to explore ways conserve improve HVAC system efficiency. One such solution the use economizer controls, which can reduce cooling consumption by using free-cooling effect. However, there are various types controls available, their effectiveness may vary depending on specific climate conditions. To investigate energy-saving potential this study employs dry-bulb temperature-based control approach. The strategy uses outdoor air temperature as an indicator whether free be used instead mechanical cooling. This also introduces artificial neural network (ANN) prediction model optimize system, lead additional savings. develop ANN model, EnergyPlus program simulation modeling, Python programming language employed development. results show that implementing reduction 7.6% annual consumption. Moreover, employing ANN-based optimal discharge air-handling units, 22.1% savings achieved. In conclusion, findings demonstrate implementation especially approach, effective reducing systems. Additionally, models further increase savings, resulting improved efficiency reduced operating costs.