What is an intelligent building? Analysis of recent interpretations from an international perspective

In recent years, the notion of intelligent buildings (IBs) has become increasingly popular due to their potentials for deploying design initiatives and emerging technologies towards maximized occupants’ comfort and well-being with sustainable design. However, various definitions, interpretations, and implications regarding the essence of IBs exist. Various key performance indicators of IBs have been proposed in different contexts. This study explores the notion of IBs and presents an analysis of their main constituents. Through a comparison of these constituents in different contexts, this study aims to extract the common features of IBs leading to an evolved definition which could be useful as a reference framework for design, evaluation, and development of future IBs. Findings also scrutinize the long run benefits of IBs, while demonstrating the constraints and challenges of the current international interpretations

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively

An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea

energie

A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings

This research presents an evaluative energy model for estimating the energy efficiency of the design choices of architects and engineers in the early design phase. We analyze the effects of various parameters with different characteristics in various combinations for building energy consumption. With this analysis, we build a database that identifies a set of heuristic rules for energy-efficient building design to facilitate the design of sustainable apartment housing. Perturbation studies are based on a sensitivity analysis used to identify the thermal influence of the input design parameters on various simulation outputs and compare the results to a reference case. Energy sensitivity weight factors are obtained from an extensive sensitivity study using building energy simulations. The results of the energy sensitivity study summarized in a set of heuristic rules for evaluating architectural features are estimated through case studies of Korean apartment buildings. This study offers valuable guidelines for developing energy-efficient residential housing in Korea and will help architects in considering appropriate design schemes and provide a ready reference to generalized test cases for both architects and engineers so that they can zero in on a set of effective design solutions

Consensus Achievement of Decentralized Sensors Using Adapted Particle Swarm Optimization Algorithm

This paper explores the possibility of enhancing consensus achievement of decentralized sensors by establishing cooperative behavior between sensor agents. To these ends, a novel particle swarm optimization framework to achieve robust consensus of decentralized sensors is devised to distribute sensing information via local fusing with neighbors rather than through centralized control; the new framework showed a 16.5 percent improvement in consensus achievement as compared to the classic majority rule method. Noteworthy enhancements in consensus achievement are also pertinent to the comparable situation of decentralized sensor systems

Multi-Criteria Performance Assessment for Semi-Transparent Photovoltaic Windows in Different Climate Contexts

Semi-transparent photovoltaic (STPV) windows, one of the building façade elements, can generate electricity and provide a certain amount of daylight for occupants. Nevertheless, expensive cost and unsatisfying indoor daylight performance in the room are common problems with STPV windows. This study investigates the thermal, daylight, energy, and life-cycle cost performance of STPV windows by considering varied window-to-wall ratios, building orientations, and STPV module types. The electricity balance index (elBI) indicator is proposed as one of the performance evaluation criteria. Two types of building models are established for this study: a rig-test building as the baseline building model and a KAIST campus research facility as the test building model along with the actual measurements and simulations using DesignBuilder. Results show that the STPV window in the Mediterranean climate demonstrates higher efficiency based on the elBI indicator. Decision-making analysis using the analytic hierarchy process and PROMETHEE II found weighting rates of 0.309, 0.076, and 0.465 for elBI, comfort, and cost criteria, respectively. Furthermore, lighting energy consumption becomes a critical variable for STPV module type selection, while a simple ON/OFF lighting control system can improve the elBI value by 0.02 ~ 0.04. Our research findings could potentially improve the decision-making process for building and urban energy systems selection in different climate types

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset

Mesoporous Structure Control of Silica in Room-Temperature Synthesis under Basic Conditions

Various types of mesoporous silica, such as continuous cubic-phase MCM-48, hexagonal-phase MCM-41, and layer-phase spherical silica particles, have been synthesized at room temperature using cetyltrimethylammonium bromide as a surfactant, ethanol as a cosurfactant, tetraethyl orthosilicate as a silica precursor, and ammonia as a condensation agent. Special care must be taken both in the filtering of the resultant solid products and in the drying process. In the drying process, further condensation of the silica after filtering was induced. As the surfactant and cosurfactant concentrations in the reaction mixture increased and the NH3 concentration decreased, under given conditions, continuous cubic MCM-48 and layered silica became the dominant phases. A cooperative synthesis mechanism, in which both the surfactant and silica were involved in the formation of mesoporous structures, provided a good explanation of the experimental results