Optimal Economizer Control of VAV System using Machine Learning

Energy efficiency of the HVAC system can be improved through renovation and operating method improvement. Economizer control, one energy efficient measures improvement method, introduces outdoor air when is sufficient for cooling. There are high/low limit that determine range economizer control mixed temperature as set-points. controlled with user's or manager's experience, set-point operated fixed. This causes problems waste because it does not consider indoor environments. Therefore, purpose this study to develop an optimal VAV resetting considering To this, a machine learning model was used predicts future state based on current state. Based developed prediction model, resets in real time evaluated simulation. As result, confirmed changed time, about 20% consumption saved compared existing dry- bulb control.