Alan S. Fung

Effects of socioeconomic factors on household appliance, lighting, and space cooling electricity consumption

Two methods are currently used to model residential energy consumption at the national or regional level: the engineering method and the conditional demand analysis (CDA) method. One of the major difficulties associated with the use of engineering models is the inclusion of consumer behaviour and socioeconomic factors that have significant effects on the residential energy consumption. The CDA method can handle socioeconomic factors if they are included in the model formulation. However, the multicollinearity problem and the need for a very large amount of data make the use of CDA models very difficult. It is shown in this paper that the neural network (NN) method can be used to model the residential energy consumption with the inclusion of socioeconomic factors. The appliances, lighting, and cooling component of the NN based energy consumption model developed for the Canadian residential sector is presented here and the effects of some socioeconomic factors on the residential energy consumption are examined using the model.

Modeling and technical feasibility analysis of a low-emission residential energy system

An energy-efficient, renewable energy-based heating, ventilating, air-conditioning and domestic hot water heating system for houses is proposed and modeled to assess its end-use energy savings potential. The system that consists of a ground heat exchanger, ground source heat pump, in-floor radiant heating, heat recovery ventilation system, photovoltaic generation and net-metered grid storage, is modeled using the open-source building simulation software ESP-r. Potential end-use energy savings of more than 50 percent are found to be attainable when the proposed heating, ventilating and air-conditioning system is compared with a conventional system to heat common energy-efficient homes in four major climatic zones of Canada. The corresponding reduction in greenhouse gas emissions (carbon dioxide equivalent) ranges from 19 percent to close to 90 percent, depending on the types of fuels replaced.

Modelling of the Net Zero Energy Town House in Toronto Using TRNSYS, and an Analysis of the Impact Using Thermal Mass

A detailed model of the Net Zero Energy Town House in Toronto is developed in TRNSYS, incorporating a ground source heat pump integrated with an in floor radiant heating system. In order to minimize the heating and cooling loads, the building envelope is well insulated with the exterior walls having an R-60 insulation value. Much of the work done previously on the use of thermal mass in buildings has been experimental in nature and has focussed mainly on conventional brick construction in hot climates such as Asia and Africa. This research will analyze the impact of using thermal mass with a building envelope that is highly insulated, and of a light construction, such as that used in Low Energy or Net Zero housing. Furthermore, this analysis would also evaluate the impact of using thermal mass in a cold climate such as that found in Canada. The simulations showed that, for colder climates, thermal mass can replace some of the insulation and still provide superior results. Also the impact of thermal mass was found to be more significant during the winter season than summer for Toronto.© 2008 ASME

Impact of energy efficiency upgrade retrofits on the residential energy consumption and Greenhouse Gas emissions in Canada

The impact of various energy efficiency upgrade scenarios on the annual energy consumption and Greenhouse Gas emissions of the Canadian housing stock is assessed using the Canadian Residential Energy End-use Model (CREEM). The energy efficiency upgrade scenarios that are considered include major retrofits, such as the improvement of the house envelope by adding insulation, and the replacement of the existing heating system and appliances by higher efficiency units, as well as minor retrofits, such as lighting fixture, thermostat, showerhead and aerator upgrades that reduce energy consumption.

A greener MAC layer protocol for smart home wireless sensor networks

The wireless sensor network (WSN) is an important element on many advanced, energy efficient data acquisition (DAQ), and control systems. Sleep/wake-up scheduling and network overhead are some of the major burdens to achieve energy efficient WSNs. In this paper, an energy efficient communication algorithm is proposed for smart home sensor networks. The proposed approach targets the wasted energy during the idle listening, collision, and overhearing processes. A three-tier Medium Access Control (MAC) protocol has been developed to minimize the total energy consumption of the network. The new protocol reduces the total energy requirement by each node, facilitates confirmed communication for steady traffic, and gives adaptive control during varying traffic load. Overall, the proposed MAC protocol shows better performance than conventional WSN protocols.

Solar-Assisted Space Heating and Small Pond-Assisted Space Cooling of a Highly Insulated Energy Efficient House

For three weeks in October 2009, the U.S. Department of Energy hosted the Solar Decathlon Competition in which 20 teams of college and university students competed to design, build, and operate their own version of a solar-powered house. Team Norths mission was to deliver North House, a compelling, marketable solar powered home for people with active lifestyles, while building Canadas next generation of leaders in sustainable engineering, business and design. This paper deals with a solar-assisted space heating system that was studied as a potential design for the competition. Among several other conclusions, it was found that using a solar-assisted in-floor heating system can decrease the energy consumption to only 8% of the case without the in-floor loop.