English

Recently, a combination between simulation and optimisation has been important for many of HVAC design problems.

Distributed energy system based on cogeneration system has high potential of energy saving due to utilizing waste heat from power generator effectively. However, unless the appropriate combination of machinery and operation are conducted, the expected performance is not achieved, it is quite difficult to determine the optimal combination of machinery and operation. Authors had already developed and proposed new optimal design method for building energy systems or distributed energy systems using Genetic Algorithm (GA) in some previous studies (e.g. Ooka R et al, 2008).

The aim of this paper is to describe the features of a Genetic Algorithm (GA) developed to solve simulation-based optimization problems for the optimal design of building parameters. This GA has been developed using guidelines from top researchs in the field of evolutionary computation. It is mostly based on NSGA-II and Omni-optimizer.

This paper presents a novel approach to derive U.S. residential building energy load profiles. This approach uses bootstrap sampling method to extract daily activity pattern of occupants of a household from American Time Use Survey (ATUS) data. The characteristics of ATUS data, the relation between time-use and load-demand, and the robustness of this approach are discussed. Virtual experiments were conducted on Energy Plus platform to study the patterns of annual load demand distribution under different household composition and thermal zoning schemes.

It is crucial to perform energy simulations during the building process in order to design a building that meets demands regarding low energy use. In a low energy building, internal heat gains such as excess heat from household electricity, are a large part of the heat balance of the building. The internal heat gains are depending on the occupants and not constant. Result from energy simulations with household electricity that varied during the day and the year according to a model based on measured data are presented.

This paper presents simulation results of three hypothetic models, which aims to represent different design decisions concerning administrative buildings at UFRN Campus, Brazil. Simulations were made with the DesignBuilder software. The analysis is intended to emphasize the influence of envelope architectural decisions on air conditioning energy consumption and the improvement of buildings thermal performance. The modelling process was supported by a field survey on 13 buildings at the Campus and by energy monitoring procedures.

By comparing EnergyPlus with other energy simulation tools, this paper explores how to use EnergyPlus to construct models to accurately simulate complex building systems as well as the interrelationships among sub-systems such as HVAC, lighting and service hot water systems. Then energy consumption and cost of a large public building is simulated and calculated for LEED certification using EnergyPlus. ASHRAE baseline model is constructed according to ASHRAE 90.1 standard and the comparison of annual energy consumption between ASHRAE baseline model and proposed model is carried out.

Time of use (TOU) electricity metering involves dividing the day, the month and the year in to slots or bands, with generally higher rates at the peak loads and low tariff rates at off-peak load periods. For this study, the statistically representative testcase Canadian house was modeled in the building energy simulation software ESP-r to estimate its sub-hourly (every fifteen minutes) electricity consumption for the appliances, lighting, domestic hot water (DHW) and space heating for an entire year.

This paper presents a method for the estimation of potential impact of climate change on the heating energy use of existing houses. The proposed method uses the house energy signature, which is developed from the current heating energy use extracted from the utility bills (e.g., for year 2007) and corresponding climatic conditions. The energy signature, which is an energy-related characteristic of the house, is used along with the outdoor air temperature predicted for 2040-2069 to forecast the heating energy use.