English

This paper deals with typical residential buildings constructed according to the national energy-efficiency standard in China’s severe cold region. Dynamic heating/cooling loads of residential building models were simulated via a factorial experiment. The electricity consumption of residential buildings was investigated, and the characteristics of the power load were analyzed statistically. The results show the cooling/heating/power load indexes of S–N- and E–W-oriented multi-story residential buildings in China’s severe cold region.

Corporate tenants require ever-greater design certainty with respect to all aspects of proposed developments. Because of this, its relative novelty and a design methodology that differs from ceiling-based Variable Air Volume (VAV) air conditioning, Under Floor Air Distribution (UFAD) has faced significant scrutiny. Building simulation offers methods to understand the implications of design decisions.

This paper depicts a formulation of the global home electricity management problem, which consists in ad-justing the electric energy consumption to the cost and availability variations of the power supply. A proposed global multi-layer solving approach includes first the computation of consumption/production coordination plans and then the dynamic matching to the actual con-sumption/production data. This paper focuses on the anticipative problem.

Residential buildings consume about a third of the UKs total energy and the need to reduce this as part of achieving the 2050 CO2 emissions targets driving the interest the modelling and performance simulation of homes. While simulation and modelling tools are in wide spread use, the detailed empirical data with which to understand the effect of systems and opera-tional complexities of households on the consumption of energy is less developed than it is for commercial buildings.

In this article, the main research goal is to present a model for rapid assessment of specific heating energy consumption of residential buildings. In the first part of this research paper the main inputs that could influence the building heating demand are identified.The next step in the development of the prediction model was to conduct dynamic simulations with different combinations of the inputs and to obtain a database of results.  With this database, a multiple regression was applied and a simple (3 inputs) and accurate (R2=0.987) model was obtained.