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Heat pumps provide an efficient heating method; however they suffer from sever capacity and performance degradation at low ambient conditions. This has deterred market penetration in cold climates. There is a continuing effort to find an efficient air source cold climate heat pump that maintains acceptable capacity and performance at low ambient conditions. Systematic optimization techniques provide a reliable approach for the design of such systems. This paper presents a step-bystep approach for the design optimization of cold climate heat pumps. We first start by describing the optimization problem: objective function, constraints, and design space. Then we illustrate how to perform this design optimization using an open source publicly available optimization toolbox. The response of the heat pump design was evaluated using a validated component based vapor compression model. This model was treated as a black box model within the optimization framework. Optimum designs for different system configurations are presented. These optimum results were further analyzed to understand the performance tradeoff and selection criteria. The paper ends with a discussion on the use of systematic optimization for the cold climate heat pump design. [ABSTRACT FROM AUTHOR]

With utility costs projected to increase in the near future, many developers and planners are beginning to view energy conservation measures more seriously. Energy modelers are going to be consulted more frequently in order to determine the feasibility of implementing these measures into future projects. However, before justified decisions can be made regarding incorporating energy conservation measures, a determination must be made on how accurate current energy modeling predications are based on actual measured data. This paper will address a study performed on four facilities comparing projected energy use versus measured data. The facilities encompass a wide range of building type, HVAC systems, climate regions, and operation schedules. The data provided will assist developers and planners with understanding the benefits of energy modeling and offer ideas to help further reduce discrepancies between modeled and metered data. [ABSTRACT FROM AUTHOR]

Modeling of energy use in existing buildings is required for different applications such as building energy management, establishing baselines, and calculating retrofit savings. A single or multivariate regression model is mostly used as a means of developing these models of energy consumption in buildings. This paper explores the applications of single and multivariate models on estimation of monthly energy consumptions in schools and discusses the several aspects of model improvements. Several regression models ranging from a simple only outdoor dry-bulb temperature to multivariate models are studied to find a comprehensive methodology in estimating energy consumptions in schools located in hot and humid climates. Data on energy consumptions in 225 schools selected from four different counties in central Florida are collected to evaluate the models and provide recommendations for improvements. The results show that by including the school scheduling in the model, the accuracy in the monthly energy consumption prediction is significantly improved comparing to that when using only temperature regression model. [ABSTRACT FROM AUTHOR]