in 2016, It was estimated that about 40% of total U.S. energy consumption was consumed by the residential and commercial sectors. According to EIA, in 2009, the energy consumption in U.S. homes was 48% which was down from 58% in 1993 Residential Energy Consumption Survey (RECS). The development of building energy savings methods and models becomes apparently more necessary for a sustainable future. The cooling coil is an essential component of HVAC systems. The accurate prediction of a cooling coil performance is important in many energy solution applications. This paper discusses the modeling methodologies of a chilled water cooling system using artificial neural networks. The objective of this research paper is to properly develop the model to predict the cooling coil performance accurately. This study utilized data from an existing building located in North Carolina, USA. Data such as chilled water supply temperature, airflow rate, mixture and supply air temperatures and humidity ratios, etc., are collected over the course of three months for developing and testing the model. Multiple neural network structures are tested along with multiple input and output delays to determine the one yielding the optimal results. Moreover, an optimization technique is developed to select premier model that can predict results accurately validated by the actual data. The observations from this research validates the use of artificial neural network model as an accurate tool for predicting the performance of a chilled water air handling unit.

Accurate modeling of total building energy is now vital to reduce energy consumption. This is especially true for buildings since they are considered as the largest energy consumer in the United States. This paper investigates modeling methods for building energy-systems using non-linear auto-regression artificial neural networks. The proposed model can forecast the whole building energy consumptions given the four input variables: Dry-bulb and wet-bulb outdoor air temperatures, hours of day and type of days. In addition, the paper presents optimization process that uses genetic algorithm to determine the best model structure by minimizing the model errors. Statistical indexes such as the root mean-square error RMSE and the coefficient of variance CV of RMSE are used to measure the model accuracy. The data was collected from existing buildings and from simulations. The collected data was used to test and train the proposed models as well as in the optimization process. Various neural network structures were tested using different inputs and feedback delays. The results show that the proposed model can accurately predict the energy consumptions. The CV values were within a range of 1.7 and 7.7%. It also proves that the model can be used for saving estimation applications and different energy efficiency.

In 2016, It was estimated that about 40% of total U.S. energy consumption was consumed by the residential and commercial sectors. According to EIA, in 2009, the energy consumption in U.S. homes was 48% which was down from 58% in 1993 (Residential Energy Consumption Survey (RECS). The development of building energy savings methods and models becomes apparently more necessary for a sustainable future. The cooling coil is an essential component of HVAC systems. The accurate prediction of a cooling coil performance is important in many energy solution applications. This paper discusses the modeling methodologies of a chilled water cooling system using artificial neural networks. The objective of this research paper is to properly develop the model to predict the cooling coil performance accurately. This study utilized data from an existing building located in North Carolina, USA. Data such as chilled water supply temperature, airflow rate, mixture and supply air temperatures and humidity ratios, etc., are collected over the course of three months for developing and testing the model. Multiple neural network structures are tested along with multiple input and output delays to determine the one yielding the optimal results. Moreover, an optimization technique is developed to select premier model that can predict results accurately validated by the actual data. The observations from this research validates the use of artificial neural network model as an accurate tool for predicting the performance of a chilled water air handling unit.