In a constantly advancing world with growing needs, buildings play in important role in the daily functioning of the society. Developing more and more advanced techniques to optimize the working of these buildings is highly important for a constant growth. Modern computational techniques have opened doors to create advanced models that can utilize efficient techniques to produce highly accurate results. This paper introduces a model that utilizes machine learning algorithms to predict energy consumption in buildings. Energy data were used from two actual and two simulated buildings to fine tune the models. The model is also compared to a baseline regression model as well as a model based on Artificial Neural Network. The results show that the proposed model performs much better than the other two compared models. The proposed model can be used for many intelligent applications such as measurement and savings verification, optimization, building-energy assessment and fault detection and diagnosis. The models were tested to predict the savings calculations for a simulated building and the results proved the proposed model to be the closest predictor to actual savings.

Different types of air-to-air energy recovery technologies such as coil loops, heat pipes, sensible wheels, and total energy wheels are frequently incorporated in HVAC systems in an attempt to reduce energy consumption. This study examines the impact of various types of energy recovery technologies and capacity control strategies on a building's cooling, heating, and fan energy consumption across different climate zones, including Fargo, ND;