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This paper presents a case study of a neighbourhood low carbon energy system designed for five off-gas rural dwellings in the UK. The employment of the neighbourhood system aims to improve energy efficiency of the whole site, reduce dependency on heating oil or LPG for off-gas houses, maximize renewable energy usage on site, and minimize fuel poverty through affordable investments. System design is discussed and built on site survey, ongoing monitoring and validated modelling. Simulation is carried out in dynamic model HTB2. A ROI analysis is used to examine the long-term cost-effectiveness, taking into account any maintenance and replacement cost, degradation of system performance and discounting of money over time. The neighbourhood system scenario is compared with an alternative scenario of separate systems for individual houses, in terms of energy reduction, energy self-sufficiency, CO2 reduction and pay-back time. The simulation results indicate the designed optimal neighbourhood system can achieve similar self-sufficiency as that of a separate system scenario, with more than 70% of its electricity demand met by onsite electricity production. Both the neighbourhood system approach and the separate one can achieve carbon negative for the whole site, with the former contributing to 31% more carbon reduction than the latter. The neighbourhood system can be paid back within its lifespan, while the separate system approach can't. The payback time of the neighbourhood system can be reduced to 14 years if traditional bolt on PV system is used instead of building integrated PV. The outcome of the research demonstrated the affordability and replicability of the neighbourhood low carbon energy system, which can decrease fuel poverty, and meet government targets for CO2 reduction.

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Occupant behavior has an important impact on building energy consumption, and the accuracy of an occupant behavior model directly affects the reliability of energy consumption simulation results. Ultra-low energy buildings are crucial to achieving building energy conservation and carbon dioxide reduction in China. In order to effectively promote the development of ultra-low energy buildings in Hot Summer and Cold Winter Climate Zones. where most residents adopt a “part-time, part-space” pattern of intermittent energy use behavior, and to solve the problem of poor indoor thermal environments and the high incremental cost of ultra-low energy, the study described in this paper takes Changsha as an example to carry out a multi-objective optimization study on ultra-low energy housing using a probabilistic behavioral model. On the basis of a probability model representing the residents’ actual behavior in Changsha, the optimization objective indicators, key variables and the technology benchmarks for ultra-low energy building were determined, then multi-objective optimization was carried out for a range of energy efficient technologies to obtain the Pareto optimal solutions. The results showed that the set of optimal solutions could reduce energy demand by 50.2 to 60.2% and reduce indoor thermal discomfort time by 3.52–11.09% compared with those of a reference base case, which just meets the requirements of the current design standard for energy efficient domestic buildings. An optimum solution for energy savings and indoor thermal comfort, along with economic costs, was identified, which can assist in decision-making by providing different preferences and provide useful reference for the design of ultra-low energy buildings in Hot Summer and Cold Winter Climate regions.