Previous studies in building energy assessment clearly state that to meet sustainable energy goals, existing buildings, as well as new buildings, will need to improve their energy efficiency. Thus, meeting energy goals relies on retrofitting existing buildings. Most building energy models are bottom-up engineering models, meaning these models calculate energy demand of individual buildings through their physical properties and energy use for specific end uses (e.g., lighting, appliances, and water heating). Researchers then scale up these model results to represent the building stock of the region studied.

Studies reveal that there is a lack of information about the building stock and associated modeling tools and this lack of knowledge affects the assessment of building energy efficiency strategies. Literature suggests that the level of complexity of energy models needs to be limited. Accuracy of these energy models can be elevated by reducing the input parameters, alleviating the need for users to make many assumptions about building construction and occupancy, among other factors. To mitigate the need for assumptions and the resulting model inaccuracies, the authors argue buildings should be described in a regional stock model with a restricted number of input parameters. One commonly-accepted method of identifying critical input parameters is sensitivity analysis, which requires a large number of runs that are both time consuming and may require high processing capacity.

This paper utilizes the Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS) model, which calculates the net energy demand of buildings and presents aggregated and individual- building-level, demand for specific end uses, e.g., heating, cooling, lighting, hot water and appliances. The model has already been validated using the Swedish, Spanish, and UK building stock data. This paper discusses potential improvements to this model by assessing the feasibility of using stepwise regression to identify the most important input parameters using the data from UK residential sector. The paper presents results of stepwise regression and compares these to sensitivity analysis; finally, the paper documents the advantages and challenges associated with each method.

Residential air conditioning systems represent a critical load for many electric

utilities, especially for those who serve customers in hot climates. In hot and dry

climates, in particular, the cooling load is usually relatively low during night hours and

early mornings and hits its maximum in the late afternoon. If electric loads could be

shifted from peak hours (e.g., late afternoon) to off-peak hours (e.g., late morning), not

only would building operation costs decrease, the need to run peaker plants, which

typically use more fossil fuels than non-peaker plants, would also decrease. Thus, shifting

electricity consumption from peak to off-peak hours promotes economic and

environmental savings. Operational and technological strategies can reduce the load

during peak hours by shifting cooling operation from on-peak hours to off-peak hours.

Although operational peak load shifting strategies such as precooling may require

mechanical cooling (e.g., in climates like Phoenix, Arizona), this cooling is less

expensive than on-peak cooling due to demand charges or time-based price plans.

Precooling is an operational shift, rather than a technological one, and is thus widely

accessible to utilities’ customer base. This dissertation compares the effects of different

precooling strategies in a Phoenix-based utility’s residential customer market and

assesses the impact of technological enhancements (e.g., energy efficiency measures and

solar photovoltaic system) on the performance of precooling. This dissertation focuses on

the operational and technological peak load shifting strategies that are feasible for

residential buildings and discusses the advantages of each in terms of peak energy

savings and residential electricity cost savings.