Development of a Building Simulation Model for Indoor Temperature Prediction and HVAC System Anomaly Detection

Abstract

In order to reduce global energy consumption, energy-efficient, green and smart buildings have to be built. In addition to the application of other energy efficiency measures, an effective management of HVAC systems is required. High quality management and control of these systems ensures optimal occupant comfort levels, proper operation, rational energy consumption, and a positive impact on the environment. This is especially important for large buildings with complex systems such as hotels. As a contribution to the creation of appropriate tools for the management and control of HVAC systems in smart buildings, this paper presents the results of the current development of a detailed dynamic simulation model based on data collected from a smart room system in a hotel in Zagreb, Croatia. The smart room system, which is integrated into the hotel's building management system, provides historical data on set and current room temperatures, room occupancy schedule, window opening, fan coil operation status, fan rotation speed, valve opening, and operating mode with a time step of 5 minutes. The simulation model based on the TRNSYS software uses a part of the available data and calculates the current internal room temperatures. A comparison of the predicted and measured temperatures at each time step showed that the deviations are within the acceptable limits. The final objectives of the model development are the identification of anomalies in the operation of the HVAC system and the optimization of its operation with the aim of reducing energy consumption.