Martti Viljanen

Prediction of indoor temperature and relative humidity using neural network models: model comparison

The use of neural networks grows great popularity in various building applications such as prediction of indoor temperature, heating load and ventilation rate. But few papers detail indoor relative humidity prediction which is an important indicator of indoor air quality, service life and energy efficiency of buildings. In this paper, the design of indoor temperature and relative humidity predictive neural networks in our test house was developed. The test house presented complicated physical features which are difficult to simulate with physical models. The work presented in this paper aimed to show the suitability of neural networks to perform predictions. Nonlinear AutoRegressive with eXternal input (NNARX) model and genetic algorithm were employed to construct networks and were detailed. The comparison between the two methods was also made. Applicability of some important mathematical validation criteria to practical reality was examined. Satisfactory results with correlation coefficients 0.998 and 0.997 for indoor temperature and relative humidity were obtained in the testing stage.

A new rapidly growing mycobacterial species, Mycobacterium murale sp. nov., isolated from the indoor walls of a children's day care centre.

Scotochromogenic mycobacterial isolates from water-damaged parts of indoor building materials of a childrens day care centre represented a phenetically and genetically distinct group of strains. A 16S rDNA dendrogram (1243 bp) showed that the closest species to the new strain MA112/96T was Mycobacterium abscessus. Phylogenetic and phenetic analyses (100 characteristics) grouped the new isolates with M. abscessus, Mycobacterium vaccae, Mycobacterium aurum and Mycobacterium austroafricanum. Ribotyping with Pvull restriction distinguished the 5 isolates from the other 12 most closely related species by the major bands at 6.5-7 kb and 13-15 kb. The cell morphology of the new isolates was typical of mycobacteria, electron microscopy revealed a triple-layered cell wall with an irregular electron-dense outer layer. They grew at 10-37 degrees C, with no growth at 45 degrees C in 5 d. The gene encoding the secreted 32 kDa protein, specific to mycobacteria, was detected by PCR. The main whole-cell fatty acids were characterized by high tuberculostearic acid 10Me-C18:0 (17% at 28 degrees C), which increased with increasing growth temperature (22% at 37 degrees C). The other main fatty acids were C18:1 cis9 and C16:0 (21-20% each), followed by, C17:1 cis9 (14%), C16:1 cis10 (8%) and also a high amount of C20 alcohol (9%). alpha-Mycolic acids, keto-mycolates and wax esters were present (C60-C90), MK-9(H2) (90%) and MK-8(H2) were the main menaquinones. The cellular phospholipids were phosphatidylethanolamine, phosphatidylinositol, phosphatidyl inositolmannosides and diphosphatidylglycerol. Polyamine content was low. G+C content was 72.9 mol%. The new isolates are proposed as a new species, Mycobacterium murale sp. nov. The type strain is MA112/96T (= DSM 44340T).

Past, present and future mathematical models for buildings

This article is the second part of a review of the historical evolution of mathematical models applied in the development of building technology. The first part described the current state of the art and contrasted various models with regard to the applications to conventional buildings and intelligent buildings. It concluded that mathematical techniques adopted in neural networks, expert systems, fuzzy logic and genetic models, that can be used to address model uncertainty, are well suited for modelling intelligent buildings. Despite the progress, the possible future development of intelligent buildings based on the current trends implies some potential limitations of these models. This paper attempts to uncover the fundamental limitations inherent in these models and provides some insights into future modelling directions, with special focus on the techniques of semiotics and chaos. Finally, by demonstrating an example of an intelligent building system with the mathematical models that have been developed for such a system, this review addresses the influences of mathematical models as a potential aid in developing intelligent buildings and perhaps even more advanced buildings for the future.

An efficient analytical solution to transient heat conduction in a one-dimensional hollow composite cylinder

In this paper a novel analytical method is applied to the problem of transient heat conduction in a one-dimensional hollow composite cylinder with a time-dependent boundary temperature. It is known that for such problems in general, the underlying eigenvalue and residue calculations pose a challenge in practice because of the computational requirements especially for a cylinder with many layers. A new approximated analytical solution is derived by a novel application of the Laplace transformation. As a result, the problem of eigenvalue or residue computation is avoided. A closed-form solution is presented. A further comparison of analytical results with numerical models demonstrates a high accuracy of the developed analytical solution.

Present and Future Mathematical Models for Buildings

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems.

Determination of salt diffusion coefficient in brick: Analytical methods

This paper presents an investigation into salt diffusion in new, fully saturated brick under isothermal conditions. A commonly used experiment methodology, diffusion cell method, is adopted. The analytical and numerical solutions are obtained. The analytical solution is simple and straightforward, which determines temporally salt concentrations in the monitored chamber. It enables us to estimate salt diffusion coefficients in a fast and accurate way.

Controlling building indoor temperature and reducing heating cost through night heating electric stove

Abstract This paper investigates the use of massive electric stove as a thermal storage in order to reduce the heating cost associated with maintaining indoor comfort condition in building. The control of electric stove takes advantage of low night-time electrical rate by shifting heating loads from day-time to night-time. A general numerical modelling of thermal behaviour of building envelope, electric stove and indoor air under the condition of outdoor variation is presented in detail. The resultant non-linear system of the heat balance equations, partial differential equations, is solved numerically. Besides the modelling work, attention is put on the control of electric stove’s night heating in such way that the stove-stored energy is released during day-time without sacrifice of indoor comfort. A simple control algorithm is given. Model applications show that the night heating with a proper size massive electric stove as a building thermal storage is cost-effective for buildings with night electrical rate benefit, while maintaining a comfortable indoor temperature in the building. Simulation also illustrates that up to 28% heating cost saving can be obtained compared with conventional direct heating.

Estimation of Space Air Change Rates and CO2 Generation Rates for Mechanically-Ventilated Buildings

It is well known that people spend 80-90% of their life time indoors. At the same time, pollution levels of indoors can be much higher than outdoor levels. Not surprisingly, the term ‘sick building syndrome’ (SBS) has been used to describe situations where occupants experience acute health and comfort effects that are related to poor air in buildings (Clements-Croome, 2000). It is an increasingly common health problem which has been acknowledged as a recognizable disease by the World Health Organization (Redlich et al., 1997, Akimenko et al., 1986). Since its recognition in 1986, many efforts have been put to try to identify the causes to eliminate SBS. The causes may involve various factors. Mainly, it is thought to be a direct outcome of poor indoor air quality (IAQ) (Clements-Croome, 2004). In most cases ventilation system is found to be at the heart of the problem as well as high carbon dioxide (CO2) levels (Redlich et al., 1997). Since 70’s energy crisis, buildings have been tried to build with tight envelopes and highly rely on mechanical ventilation so as to reduce energy cost. Due to tight envelopes, a big portion of energy contributes to ventilation. In most cases SBS occurs in mechanically-ventilated and commercial buildings, although it may occur in other buildings such as apartment buildings. It has been estimated that up to 30% of refurbished buildings and a significant number of new buildings suffer from SBS (Sykes, 1988). However, the solutions to SBS are difficult to implement by the complexity of ventilation system and the competing needs of energy saving. Hence the issue about ventilation efficiency is getting more and more people’s attention. It is useful to evaluate ventilation in order to assess IAQ and energy cost. A number of techniques are available to perform such evaluations. Among them, the measurement and analysis of CO2 concentrations to evaluate specific aspects of IAQ and ventilation is most emphasized. CO2 is a common air constituent but it may cause some heath problems when its concentration level is very high. Normally CO2 is not considered as a causal factor in human health responses. However, in recent literalities, it has been reported that there is a statistically significant association of mucous membrane (dry eyes, sore throat, nose congestion, sneezing) and lower respiratory related symptoms (tight chest, short breath, cough and wheeze) with increasing CO2 levels above outdoor levels (Erdmann & Apte,

A novel and efficient analytical method for calculation of the transient temperature field in a multi-dimensional composite slab

This paper provides an efficient analytical tool for solving the heat conduction equation in a multi-dimensional composite slab subject to generally time-dependent boundary conditions. A temporal Laplace transformation and novel separation of variables are applied to the heat equation. The time-dependent boundary conditions are approximated with Fourier series. Taking advantage of the periodic properties of Fourier series, the corresponding analytical solution is obtained and expressed explicitly through employing variable transformations. For such conduction problems, nearly all the published works necessitate numerical work such as computing residues or searching for eigenvalues even for a one-dimensional composite slab. In this paper, the proposed method involves no numerical iteration. The final closed form solution is straightforward; hence, the physical parameters are clearly shown in the formula. The accuracy of the developed analytical method is demonstrated by comparison with numerical calculations.

Energy Efficiency Assessment for Data Center in Finland: Case Study

As the data-driven economy grows, we are facing unprecedented challenges of improving energy efficiency in data centers. Minimising the cooling energy demand in data centers is one of the main objectives. This paper investigates overall energy consumption and the energy efficiency of cooling system for a data center in Finland as a case study. The temporal energy consumption characteristics, cooling infrastructure and operation of the data center are analysed. The main problems about cooling energy efficiency and the factors that may contribute toward higher efficiency are identified and further suggestions are put forward. Results are presented of an extensive evaluation of the energy performance of the study data center with a view to energy recovery. The conclusion we can draw is that even though the analysed data center demonstrated relatively high energy efficiency, based on its power usage effectiveness value, there is still energy saving potential.