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Energy models of buildings can be developed and used for analysis of energy consumption. A model offers the opportunity to simulate a building under specific conditions for analysis of energy efficiency measures or optimum design. Due to the great amount of information needed to develop an energy model of a building, the number of inputs can be reduced by making variable the most relevant input parameters and making the others to take common or standard values. In this study, an analysis of input parameters required by computational tools to estimate energy consumption in homes was done in two stages. In the first stage, common input parameters were identified for three software and three webtools based on the criteria that the input parameter should be common for at least two software and at least one webtool. In the second stage, a sensitivity analysis was performed on the inputs identified in the first stage. The software BEopt, developed by the National Renewable Energy Laboratory, was used as the source of typical input parameters to be compared, and to perform the simulations for the sensitivity analysis. The base or reference model to perform simulations for the sensitivity analysis corresponds to a model developed with information from a research house located on the campus of the University of Texas at Tyler and default inputs for the BEopt B-10 reference benchmark. Results show that besides the location, and consequently the weather, common parameters are building orientation, air leakage, space conditioning settings, space conditioning schedule, water heating equipment, and terrain. Among these parameters, the sensitivity analysis identified the largest variations in energy consumption for variations on space conditioning schedule (heating and cooling setpoints), followed by the type of water heating equipment. [ABSTRACT FROM AUTHOR]

The considerable amount of energy consumption associated to the residential sector justifies and supports energy consumption modeling efforts. Among the three approaches to develop energy models, statistical approaches are a good option to avoid the burden associated to engineering approaches when observed/measured data is available. Among the statistical models, the linear regression analysis has shown promising results because of the reasonable accuracy and relatively simple implementation when compared to other methods. In this study, simple and multiple linear regression analysis along with a quadratic regression analysis were performed on hourly and daily data from a research house. The time interval of the observed data showed to be a relevant factor defining the quality of the model. Multiple linear regression models using the outdoor temperature and solar radiation offered improved coefficient of determination, but deteriorated root mean square error emphasizing the importance of using both parameters to assess and compare models. The content and structure of the paper has been devised to become a comprehensive material to be considered as the starting point for future work in this interesting research area. This paper also conveys the authors׳ belief that the future of residential energy forecasting is moving toward the development of individual models for each household due to the availability of data from smart meters, as well as the development of friendly and easy-to-use engineering software. [ABSTRACT FROM AUTHOR]

Some of the challenges to predict energy utilization has gained recognition in the residential sector due to the significant energy consumption in recent decades. However, the modeling of residential building energy consumption is still underdeveloped for optimal and robust solutions while this research area has become of greater relevance with significant advances in computation and simulation. Such advances include the advent of artificial intelligence research in statistical model development. Artificial neural network has emerged as a key method to address the issue of nonlinearity of building energy data and the robust calculation of large and dynamic data. The development and validation of such models on one of the TxAIRE Research houses has been demonstrated in this paper. The TxAIRE houses have been designed to serve as realistic test facilities for demonstrating new technologies. The input variables used from the house data include number of days, outdoor temperature and solar radiation while the output variables are house and heat pump energy consumption. The models based on Levenberg-Marquardt and OWO-Newton algorithms had promising results of coefficients of determination within 0.87–0.91, which is comparable to prior literature. Further work will be explored to develop a robust model for residential building application. [ABSTRACT FROM AUTHOR]