This study provides an open-source dataset of the measured weather data, building indoor data, and system data under the different test settings. The test building is the two-story Flexible Research Platform building at the US Department of Energy’s Oak Ridge National Laboratory, in Oak Ridge, Tennessee. Four heating tests and three cooling tests were conducted. The 1-min interval of weather, building indoor data, and system data from each test setting are provided. Actual weather data were collected from a weather station installed on the roof. This paper describes information on the test building and installed sensors, data collection method, and data validation. The provided dataset can be employed to understand HVAC system conditions and building indoor conditions under different HVAC system operations and the performance of building envelope without HVAC system operation using free-floating test data. Additionally, it can be used for empirical validation of the building energy modelling engine.

This paper developed an algorithm that controls the supply air temperature in the variable refrigerant flow (VRF), outdoor air processing unit (OAP) system, according to indoor and outdoor temperature and humidity, and verified the effects after applying the algorithm to real buildings. The VRF-OAP system refers to a heating, ventilation, and air conditioning (HVAC) system to complement a ventilation function, which is not provided in the VRF system. It is a system that supplies air indoors by heat treatment of outdoor air through the OAP, as a number of indoor units and OAPs are connected to the outdoor unit in the VRF system simultaneously. This paper conducted experiments with regard to changes in efficiency and the cooling capabilities of each unit and system according to supply air temperature in the OAP using a multicalorimeter. Based on these results, an algorithm that controlled the temperature of the supply air in the OAP was developed considering indoor and outdoor temperatures and humidity. The algorithm was applied in the test building to verify the effects of energy reduction and the effects on indoor temperature and humidity. Loads were changed by adjusting the number of conditioned rooms to verify the effect of the algorithm according to various load conditions. In the field test results, the energy reduction effect was approximately 15–17% at a 100% load, and 4–20% at a 75% load. However, no significant effects were shown at a 50% load. The indoor temperature and humidity reached a comfortable level.

Automated fault detection and diagnosis (AFDD) tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, access to high-quality training data for such algorithms is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus^® and OpenStudio^® to generate a cost-effective training data set for developing AFDD algorithms. Part II (this paper) first presents a methodology of validating fault models with OpenStudio and then presents validation results, which are compared against measurements from a reference building. We discuss the results of our experiments with eight different faults in the reference building (a total of 39 different baseline and faulted scenarios), including our methodology for using fault models along with the reference building model to simulate the same faulted scenarios. Then, we present validation of the fault models by comparing results of simulations and experiments either quantitatively or qualitatively.