New directions for housing research due to climate change in New Zealand

Research concerned with energy and housing in NZ has focussed on the costs-effectiveness of maintaining warmth. Studies have concentrated on heat loss from houses and the efficiency of heating systems. One of the consequences of this has been Government subsidies for insulation and heat pump installations to reduce energy consumption in winter months. This has led to a significant growth in the heat pump market. Research is indicating that these devices are not significantly decreasing the demand for electricity in the winter. Of greater concern is that there is an increase in demand for electricity for cooling purposes which introduces a new and significant electrical load in the summer. This paper will outline the research currently being undertaken on the long-term impact of both climate change and energy depletion and the consequences for Building Code standards and ‘sustainability’ rating tools for housing. In New Zealand there has been a general shift in peak electrical demand from winter towards summer which has increased the risk of inadequate supplies in summer months. Climate change will not only alter the seasonal demand for electricity it will also impact on seasonal supply. About 50% of the water used for hydro electricity generation comes from glacial melt-water during the summer. The glaciers are now retreating due to climate change and it has been estimated that most glaciers will have melted by about 2040. NZ will not only experience ‘peak oil’ and ‘peak gas’ but also ‘peak hydro’. This will significantly increase the cost of electricity and the risk of interrupted supplies. The paper concludes that consideration should be given to subsidising long-lasting improvements to the fabric of houses rather than subsidising short-lived equipment such as heat pumps. Rating tools for the ‘sustainability’ of new and refurbished housing should also address this problem and actively discourage equipment that results, not only in increased electricity consumption, but also does not allow the human body the ability to adapt over time to the predicted increased average temperatures in New Zealand

Energy and Smart Growth: It's about How and Where We Build

By efficiently locating development, smarter growth land use policies and practices offer a viable way to reduce U.S. energy consumption. Moreover, by increasing attention on how we build, in addition to where we build, smart growth could become even more energy smart. The smart growth and energy efficiency movements thus are intrinsically linked, yet these two fields have mostly operated in separate worlds. Through greater use of energy efficient design, and renewable energy resources, the smart growth movement could better achieve its goals of environmental protection, economic security and prosperity, and community livability. In short, green building and smart growth should go hand in hand. Heightened concern about foreign oil dependence, climate change, and other ill effects of fossil fuel usage makes the energy-smart growth collaboration especially important. Strengthening this collaboration will involve overcoming some hurdles, however, and funders can play an important role in assisting these movements to gain strength from each other. This paper contends there is much to be gained by expanding the smart growth movement to include greater attention on energy. It provides a brief background on current energy trends and programs, relevant to smart growth. It then presents a framework for understanding the connections between energy and land use which focuses on two primary issues: how to build, which involves neighborhood and building design, and where to build, meaning that location matters. The final section offers suggestions to funders interesting in helping accelerate the merger of these fields

Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task

Pre- and Post-Occupancy Evaluation of Resident Motivations for and Experiences of Establishing a Home in a Low-Carbon Development

There is some understanding of how an individual’s daily practices consume resources in the home, but the home as a space itself and peoples’ relationships to it remain an interesting research area. In this paper, residents of an Australian low-carbon development (LCD) are studied in order to discover the expectations and motivations driving them to move to their new home, the emotional landscape of the home, and their subsequent experiences living in an LCD. This exploration through mixed methods and a post-occupancy evaluation enables a longitudinal empirical study of the motivations, perceptions, expectations and experiences of an LCD residence. This study aims to further conceptualize the social understanding of a home and what people consider when moving into an LCD, along with the post-occupancy experiences that are important for establishing LCDs in the future. The results show that a home is associated with being a place of community, sustainability, safety and comfort, as well as a place that incorporates aesthetically pleasing features. The motivation for residents moving into an LCD is to have housing stability, live the life they want (including performing sustainable practices) and enjoy the attractive design of the LCD. The user experiences of living in an LCD include unexpected design influences on daily practices and an appreciation of the community atmosphere created. The strong sense of community and the self-reported thermally comfortable homes met residents’ expectations post-occupancy. This research is of interest to academics in the low-carbon and social science sectors, real-estate agents and property developers, as it provides insight into motivations and expectations of low-carbon dwelling residents

Information technology and social cohesion : a tale of two villages

Acknowledgements This research was made possible by a grant from the EPSRC “Dot.Rural Digital Economy Hub” (EP/G066051/1) at the University of Aberdeen and EPSRC Communities and Culture Network+ (EP/K003585/1).Peer reviewedPostprin

PERFORMANCE AND APPLICATIONS OF RESIDENTIAL BUILDING ENERGY GREY-BOX MODELS

The electricity market is in need of a method to accurately predict how much peak load is removable by directly controlling residential thermostats. Utilities have been experimenting with residential demand response programs for the last decade, but inconsistent forecasting is preventing them from becoming a dependent electricity grid management tool. This dissertation documents the use of building energy models to forecast both general residential energy consumption and removable air conditioning loads. In the models, complex buildings are represented as simple grey-box systems where the sensible energy of the entire indoor environment is balanced with the flow of energy through the envelope. When internet-connected thermostat and local weather data are inputs, twelve coefficients representing building parameters are used to non-dimensionalize the heat transfer equations governing this system. The model's performance was tested using 559 thermostats from 83 zip codes nationwide during both heating and cooling seasons. For this set, the average RMS error between the modeled and measured indoor air temperature was 0.44°C and the average daily ON time prediction was 1.9% higher than the data. When combined with smart power meter data from 250 homes in Houston, TX in the summer of 2012 these models outperformed the best traditional methods by 3.4 and 28.2% predicting daily and hourly energy consumption with RMS errors of 86 and 163 MWh. The second model that was developed used only smart meter and local weather data to predict loads. It operated by correlating an effective heat transfer metric to past energy data, and even further improvement forecasting loads were observed. During a demand response trial with Earth Networks and CenterPoint Energy in the summer of 2012, 206 internet-connected thermostats were controlled to reduce peak loads by an average of 1.13 kW. The thermostat building energy models averaged forecasting the load in the 2 hours before, during, and after these demand response tests to within 5.9%. These building energy models were also applied to generate thermostat setpoint schedules that improved the energy efficiency of homes, disaggregate loads for home efficiency scorecards and remote energy audits, and as simulation tools to test schedule changes and hardware upgrades