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Data mining analysis of building simulation performance data
Abstract
Detailed simulation studies of building performance can result in large data sets, particularly where statistical information on annual energy or environmental performance is required. Key performance indicators such as the number of hours above a certain temperature can easily be extracted. However, it is difficult for users to explore such datasets and understand the underlying reasons why a building performs in a certain way. This is especially true in climate responsive buildings which involve complex interactions of ventilation, solar gains, internal gains and thermal mass, for example. Data mining techniques have traditionally been employed in the financial and marketing sectors to elicit patterns within the data. This paper describes how the different data mining techniques may be employed in helping to analyse building performance data. Clustering is identified as a particular useful analysis technique and its potential is illustrated through a number of case studies.
... Data mining techniques is a way to extract patterns (knowledge) from data (Alnoukari, Alzoabi, and Hanna, 2008;Morbitzer, Strachan, and Simpson, 2004). We are using the terms knowledge and pattern interchangeably as patterns inside the huge databases reflect what-so-called "organizational tacit knowledge". ...
... Data mining algorithms are separated into two main classes (Garcia, Roman, Penalvo, and Bonilla, 2008;Morbitzer, Strachan, and Simpson, 2004): ...
... Different data mining techniques are used in conjunction with simulation data analysis (Morbitzer, Strachan, and Simpson, 2004;Painter, Erraguntla, Hogg, and Beachkofski, 2006): ...
Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education
... Data mining techniques is a way to extract patterns (knowledge) from data (Alnoukari, Alzoabi, and Hanna, 2008;Morbitzer, Strachan, and Simpson, 2004). We are using the terms knowledge and pattern interchangeably as patterns inside the huge databases reflect what-so-called "organizational tacit knowledge". ...
... Data mining algorithms are separated into two main classes (Garcia, Roman, Penalvo, and Bonilla, 2008;Morbitzer, Strachan, and Simpson, 2004): ...
... Different data mining techniques are used in conjunction with simulation data analysis (Morbitzer, Strachan, and Simpson, 2004;Painter, Erraguntla, Hogg, and Beachkofski, 2006): ...
... Clustering techniques have also been applied to analyze the data sets generated by building automation systems. Xaio and Fan [35] used cluster analysis to identify daily power consumption patterns, whereas Morbitzer et al. [36] applied clustering to analyze simulation results for performance predictions in order to extract predicted operation rules. ...
... Clustering techniques are extremely e↵ective in the analysis of correlations between building infrastructure and performance. For this purpose, Morbitzer et al. [36] applied clustering algorithms to process building monitoring data and discover non-obvious factors of energy loss in building infrastructures. However, clustering is not the only technique that has been employed. ...
The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.
... This task is descriptive rather than predictive and the goal is to detect trends in the current data without requiring prior learning. Different techniques of these algorithms are used to analyze the simulation data [1], [8]: ...
... The authors in [8] describe how the different data mining techniques can be used to assist in analyzing the performance of the predictions obtained from the simulation scenarios. In this work, the authors identified the clustering as a useful technical analysis and appropriate to analyze the data from the simulation. ...
Researchers take years and even decades of observation in order to analyze socio-economic phenomenon. Whereas the agent-based modeling simulation (ABMS) provides a new issue by offering the possibility to create virtual societies in which individuals and organizations are directly represented with their observed interactions. As it is known simulation generates and consumes a large amount of data. The analysis of these data which may contain implicit and hidden information will always remain a very difficult phase in the ABMS. As a solution to this problematic, the use of data mining techniques can contribute to the right analysis of the phenomena that emerges in these systems. In this paper we aim the investigation of agent-based modeling simulation and data mining techniques.
... It has considered data applications to do computational tasks. Examples of these tasks are briefly mentioned as follows: efficient operations for predicting energy demand and building operations, e.g., [28]- [32]; retrofitting analysis to achieve sustainable development of EMS, e.g., [33]- [35]; verification procedures to detect faults by monitoring building, e.g., [36]- [40]; economic analysis of energy-use by performing statistical operations to do classification, clustering, and pattern analysis to come up with a summary that understands how and when occupants/ customers need to use more energy, e.g., [41]- [46]; and falsified data injection detection and nontechnical losses e.g., [47]- [53]. ...
Energy management systems in buildings (EMSs-in-Bs) play key roles in energy saving and management to which an efficient energy management system in buildings (EMS-in-Bs) design contributes. Different scope-based designs of EMS-in-Bs are reviewed. The objective is to highlight different scope-based designs of EMS-in-Bs in which scopes of reviewed papers aim to implement a
function
of, for example, “monitor energy performance”, “estimate energy-use”, or “control energy-use”. This paper aims to constitute a comprehensive conception of how efficient such an EMS-in-Bs to perform more than one scope (i.e., function). Meaning, is the proposed EMS-in-Bs able to perform several sequential functions? This paper’s contribution is to give a function-focused EMS’s review utilizing the scope of reviewed papers. That is, reviewed papers are classified based on the scope/function the selected EMS-in-Bs is designed for. This could help select an EMS-in-Bs to perform certain scope/function(s). Another contribution is that, numerous EMSs-in-Bs are reviewed in a classified way so that the most adequate EMS-in-Bs for a certain scenario considering the performed scopes/functions e.g., “monitor” are highlighted. Findings showed that “control-optimize”-functioned EMS-in-Bs achieved highest energy-saving rates ~30% compared to “estimate-predict” with 10%. Findings, insights given by reviewed studies, current problems faced, future directions, and remarks are drawn in conclusion. Analysis done on reviewed papers has found that the highest and lowest averaged-energy saving rates were obtained with papers whose their scopes are implementing “control”-with-“optimize” and “estimate”-with-“predict”, respectively. Energy saving rates for these two classes of scopes have been equal to 22.57% and 10%, respectively. We recommend that there is a need to enhance the estimation- and prediction-related EMS-in-Bs to achieve a higher energy saving rate.
... Characterization of electric energy consumers was acquired using knowledge mining [10]. It 80 was also used to analyze data collected from simulations [11], or wireless sensor networks [12]. Most of these studies focus on the energy consumption of buildings, but few evaluate occupant related aspects of building performance or the geometrical information of the buildings. ...
In this paper, we focus on the building performance assessment using big data and visual analytics techniques driven by building occupancy. Building occupancy is a paramount factor in building performance, specifically lighting, plug loads and HVAC equipment utilization. Extrapolation of patterns from big data sets, which consist of building information, energy consumption, environmental measurements and namely occupancy information, is a powerful analysis technique to extract useful semantic information about building performance. To this end, visual analytics techniques are exploited to visualize them in a compact and comprehensive way taking into account properties of human cognition, perception and sense making. Visual Analytics facilitates the detailed spatiotemporal analysis building performance in terms of occupancy comfort, building performance and energy consumption and exploits innovative data mining techniques and mechanisms to allow analysts to detect patterns and crucial point that are difficult to be detected otherwise, thus assisting them to further optimize the building’s operation. The presented tool has been tested on real data information acquired from a building located at southern Europe demonstrating its effectiveness and its usability for building managers.
... Characterisation of electric energy consumers was acquired using data mining (Figueiredo et al. 2005). It was also used to analyse data collected from simulations (Morbitzer et al. 2004), or wireless sensor networks (Wu & Clements-Croom 2007). ...
... A simple example of applying statistics to investigate output thermal simulation results is proposed by Ghiaus and Allard 2003, who assess building adaptability through regression considering the free-run internal building temperature and the outside air temperature (Figure 7). A more elaborate example of statistics application to analyse thermal simulation results is provided by Morbitzer et al 2003, who considered the analysis of more than one parameter affecting performance through the use of data mining. Data mining is a combination of visual investigation, regression techniques and uncertainty analysis which basically consists of combining data sources, selecting the task relevant data and extracting patterns from this data through a user defined technique (Figure 8). ...
This article critically examines the main trends in attempting the integration of building thermal simulation tools throughout the whole building design process, focusing on studies related to building design only, not addressing studies related to heating, ventilation and air conditioning (HVAC) and servicing engineering design. It presents a review of the research literature on the issue showing that, so far, attempts have been concentrated in propositions to improve thermal simulation tools data interpretation as well as propositions to improve the role of tools in building design practice. Examples of the literature related to the two topics are critically examined by considering their effectiveness in addressing the interdisciplinary problem of integration. This critical examination leads to a thorough mapping of specific reasons about why integration is not happening, complementing the current information provided from empirical studies on the matter. Even though the author recognizes integrated design should account for HVAC and servicing, it is necessary to first have a discussion that addresses assimilating simulation tools into the design process if proper integrated design is to happen.
... Data Mining and the KDD processes research in building performance analysis is rare and only beginning to be developed in the built environment data analysis domain. Morbitzer et al. [14] applied data mining to obtain information from simulation results. Their approach uses clustering as a particular useful analysis technique and illustrates its potential in enhancing the analysis of building simulation performance predictions. ...
Performance monitoring using wireless sensors is now common practice in building operation and maintenance and generates a large amount of building specific data. However, it is difficult for occupants, owners and operators to explore such data and understand underlying patterns. This is especially true in buildings which involve complex interactions, such as ventilation, solar gains, internal gains and thermal mass. Performance monitoring requires collecting data concerning energy consumption and ambient environmental conditions to model and optimise buildings' energy consumption. This paper details the use of data mining techniques in understanding building energy performance of geothermal, solar and gas burning energy systems. The paper is part of an outgoing research into optimisation of building performance under hybrid energy regimes. The objective of the research presented in this paper is to predict comfort levels based on the Heating, Ventilating, and Air Conditioning (HVAC) system performance and external environmental conditions. A C4.5 classification methodology is used to analyse a combination of internal and external ambient conditions. The mining algorithms are used to determine comfort constraints and the influence of external conditions on a building's internal user comfort. To test the performance of classification and its use in prediction, different offices, one to the south and the other to the north of the building are used. Classification rules being developed are analysed for their application to modify control algorithms and to apply results to generalise hybrid system performance. The results of this study can be generalised for an entire building, or a set of buildings, under a single energy network subject to the same constraints.
... Some DM techniques, such as neural networks and case-based reasoning, have been adopted in intelligent decision support systems to support the execution of construction projects, such as site layout [16] , biding decision making [17] , procurement approach selection [18] , and construction management [10] . Some fundamental guidelines for applying DM in the construction industry have also been developed in the research community19202122 . Self-learning is the most important characteristic that makes DM-based systems superior to traditional expert systems. ...
Problem-solving processes in value management (VM) workshops in the construction industry are experience-based, and the quality of these workshops depends very much on the experience of the team members. The efficiency and effectiveness of VM workshops can be improved by better reusing the experience of previous VM cases and field knowledge. This paper describes a new approach to facilitate VM workshops in the construction industry using data mining (DM) techniques. The feasibility of integrating DM techniques with VM workshops in the construction industry is demonstrated in case studies. Examples are presented to illustrate different methods of applying DM tools in VM workshops. The results show that DM techniques can help team members in VM workshops to understand their problems more clearly and to generate more ideas for current problems.
... Characterisation of electric energy consumers was acquired using DM [30]. DM was also used to analyze data collected from simulations [31], and wireless sensor networks [32]. Additionally, occupants' thermal comfort is determined through the use of DM [33]. ...
In this paper, data mining techniques are coupled with internal daylight analytical tools. The aim is to examine the benefits of these techniques in assisting designers' decision making and improving scalability and applicability of indoor daylight methods. Techniques were verified using lowest annual daylight factor values, resulting from the worst case scenario in Ecotect, for rooms in the Environmental Research Institute building, Ireland. These values were used, along with real time hourly environmental conditions, sampled at 15 min intervals, during working hours, over 3 years, for the development of several prediction models. Results show that techniques perform better than individual analytical tools. Attribute importance models bring out essential building characteristics required to estimate performance for optimized room and building design. The number of simulation prototypes is reduced and need for sensing equipment is eliminated to determine indoor daylight levels by regression analysis. Acceleration of the assessment of building performance using different daylight design criteria can be observed by the use of classification modeling.
... The purpose is to exploit DM to analyze buildings' monitoring data to estimate energy consumption [28][29][30]. This includes human-environment behavior in laboratory and simulation methods [13,31,32], which lack the quantity and quality of real time data. Unfortunately, most adaptive thermal approaches integrate the IB concepts poorly in their totality, e.g. ...
This research investigates the impact of connecting building characteristics and designs with its performance by data mining techniques, hence the appropriateness of a room in relation to energy efficiency. Mining models are developed by the use of comparable analytical methods. Performance of prediction models is estimated by cross validation consisting of holding a fraction of observations out as a test set. The derived results show the high accuracy and reliability of these techniques in predicting low-energy comfortable rooms. The results are extended to show the benefits of these techniques in optimizing a building's four basic elements (structure, systems, services and management) and the interrelationships between them. These techniques extend and enhance, current methodologies, to simplify modeling interior daylight and thermal comfort, to further assist building energy management decision-making.
Traditional parametric methods are focused on understanding the behavior of the underlying probabilistic process/system which generated the data by relying on the model structure identified. In contrast, data analytic methods (DAM), which include data mining (DM) and machine learning (ML), are directly concerned with practical applications (such as discerning hidden information, discovering patterns, associations, and trends, or summarizing data behavior) through data exploration and computer-based statistical learning algorithms. Most of this chapter discusses two widely used classes of DAM, namely classification and clustering. Anomaly detection methods are also covered, albeit briefly. Several case study examples from published research papers are provided for a better and more pragmatic understanding.
Clustering methods are unsupervised learning algorithms by which samples or objects can be automatically separated or partitioned into subsets of greater homogeneity or grouped and agglomerated at different levels of aggregation, based on some predetermined similarity criteria. The two types of clustering techniques, namely, partitional and hierarchical, are described and illustrative examples provided. The two main subtypes of partitional clustering algorithms, namely, centroid-based (the popular K-means) and density-based (the widely used DBSCAN method), are treated and their applicability, advantages, and disadvantages pointed out.
Classification methods consist of a group of supervised learning methods for situations when data objects specified by their class labels and attribute set must be assigned to different groups. This chapter covers the two main subtypes, namely, the statistical-based and tree-based classification approaches. In the former, the following approaches are treated: distance-based (k-nearest neighbor), naïve Bayesian, regression-based, discriminant function analysis, neural network-based radial basis function, and the widely popular support vector machines. Tree-based methods are powerful, nonlinear methods that work by successively dividing the input feature (variable) space into smaller and smaller distinct and non-overlapping sub-regions following a set of if-then rules. The splitting is often done by criteria such as the Gini index and the entropy measure. The tree originates from a root node and splits into successively smaller branches terminating in leaf nodes. These sub-regions represent the most homogeneous response to the predictor and modeling them by a simple model is the basis of the popular classification and regression tree (CART) method. However, a single decision tree is susceptible to noise (overfitting) and is considered to suffer from high variance; that is, it is affected materially by the specific set of training data. This problem is overcome by a bootstrap method which allows an ensemble of trees to be generated. Such an approach is the random forest (RF) which is very effective for complex classification problems since it capitalizes on very flexible fitting procedures that can respond to highly local features of the data.
In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.
Establishing quality management systems within an organization has a goal to achieve total performance and sustainable development. New release of ISO 9001:2015 standard has brought rudimentary changes, making opportunities for effective accomplishment of these goals. Numerous researches on this topic highlight the fact that the use of a technique for knowledge discovery has a big impact on the fulfilment of requests set by ISO 9001 standards. Based on the results of this research, the authors decided to investigate a possibility for creation of tools for knowledge discovery through the respect of principles, requests and recommendations of this standard. The development of such a tool (simulator) would have a goal to predict future states of business processes of an organization, which is the base for performing an efficient risk estimation and optimization of business processes. This paper describes an approach to the development of such a tool (simulator) based on key business processes in an organization and data on realization of these processes during the time, and placing them into a context by selected statistical methods.
Building facility managers are increasingly equipping their buildings with extensive sets of sensors. This article aims to develop an analysis decision-making methodology based on the production of statistical indicators. The tracking of such indicators allows detecting any systems performance problems. The automatic pinpointing of malfunctions can serve to activate alerts. Our approach focuses on the processing of data stemming from secondary schools managed by departmental services in the Pas-de-Calais, where 117 secondary school buildings have been instrumented with various sensors and supplying data since 2015. This article starts with a close-up on data mining for water, gas and electricity consumption. Data mining and machine learning methods, including the Clustering approach (K-Means), have been used to extract information from the measurements conducted in 2015 and 2016. This information is used to classify the 2017 measurements according to supervised approaches (SVM). The specificity of this work is to delve deeper into the analysis by combining into the same algorithm a set of various sensors related to both energy use and building occupancy. The data classification results have allowed highlighting "atypical" operations during the daytime, through interpreting data classification results in an effort to define the status of every day in year 2017.
Computational fluid dynamic (CFD) simulations present numerous challenges in the domain of artificial intelligence. Computational time, resources and cost that can reach disproportional size before leading a simulation to its fully converged solution are one of the central issues in this domain. In this paper, we propose a novel algorithm that finds optimal parameter settings for the numerical solvers of CFD software. Indeed, this research proposes an alternative approach; rather than going deeper in reducing the mathematical complexity, it suggests taking advantage of the history of previous runs in order to estimate the best parameters for numerical equation resolution. In fact, our approach is bio-inspired and based on a genetic algorithm (GA) and evolutionary strategies enhanced with surrogate functions based on machine-learning meta-models. Our research method was tested on 11 different use cases using various configurations of the GA and algorithms of machine learning such as regression trees extra trees regressors and random forest regressors. Our approach has achieved better runtime performance and higher convergence quality (an improvement varying between 8 and 40%) in all of the test cases when compared to a basic approach which requires manually selecting the parameters. Moreover, our approach outperforms in some cases manual selection of parameters by reaching convergent solutions that couldn’t otherwise be achieved manually.
In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.
Occupant behavior has significant impacts on building energy performance and occupant comfort. However, occupant behavior is not well understood and is often oversimplified in the building life cycle, due to its stochastic, diverse, complex, and interdisciplinary nature. The use of simplified methods or tools to quantify the impacts of occupant behavior in building performance simulations significantly contributes to performance gaps between simulated models and actual building energy consumption. Therefore, it is crucial to understand occupant behavior in a comprehensive way, integrating qualitative approaches and data- and model-driven quantitative approaches, and employing appropriate tools to guide the design and operation of low-energy residential and commercial buildings that integrate technological and human dimensions. This paper presents ten questions, highlighting some of the most important issues regarding concepts, applications, and methodologies in occupant behavior research. The proposed questions and answers aim to provide insights into occupant behavior for current and future researchers, designers, and policy makers, and most importantly, to inspire innovative research and applications to increase energy efficiency and reduce energy use in buildings.
This paper takes modern marketing and data mining as basic theory basis, applying association mining algorithm to marketing data analysis with specific characteristics in industry and establishes data mining-based marketing analysis method model. This method model comprehensively discovers neural network and data mining technology to perform law discovery in historical data warehouse. The basic association rules analysis is expanded to multi-dimension and FP-Growth algorithm is improved on the basis of association rule mining principle. Furthermore, it is applied to analyze hierarchical relationship of price change between various factors inside the given market environment. Finally, power market marketing case is taken as test object and the scheme effectiveness of this paper is verified.
Simulation is a popular methodology for analyzing complex manufacturing environments. According to the large number of output of simulations, interpreting them seems impossible. In this paper we use an innovative methodology that combines simulation and data mining techniques to discover knowledge that can be derived from results of simulations. Data used in simulation process, are independent and identically distributed with a normal distribution, but the output data from simulations are often not i.i.d. normal. Therefore by finding associations between output data mining techniques can operate well. Analyzers change the sequences and values of input data according to the importance they have. These operations optimize the simulation output analysis. The methods presented here will of most interest to those analysts wishing to extract much information from their simulation models. The proposed approach has been implemented and run on a supply chain system simulation. The results show optimizations on analysis of simulation output of the mentioned system. Simulation results show high improvement in proposed approach.
Data mining is to discover problems in the large amounts of data. Also, data mining trying to find the cause of the problem and the structure. Building energy consumption patterns, the amount of data is infinite. Also, the patterns have a lot of direct and indirect effects. Discussion is needed about the correlation. This work looking for the cause of energy consumption. As a result, energy management can find out the issue. Building energy analysis utilizing data mining techniques to predict energy consumption. And the results are as follows: 1) Using data mining technique, We classified complicated data to several patterns and gained meaningful informations from them. 2) Using cluster analysis, We classified building energy consumption data of residents and analyzed characters of patterns.
Data stream technique mainly includes data stream management technique and data stream mining technique. Both can be applied to simulation system. In this paper, we proposed an on-line evaluation framework for simulation system based on data stream management technique and a simulation application framework based on data stream mining, and design general data-streams-mining federate to make it easy to integrate the various data streams mining algorithms in HLA-architecture-based simulation system quickly, and introduce our general federate for mining association rule in data streams with a simulation system for missile penetration.
This paper contrasts two different paradigms of design thinking: the one of the dynamic thermal simulation tool users with the one of the building designer. It shows that, in theory, the two paradigms seem to be incommensurable but complementary due to differences in knowledge and praxis between the two professions. The author discusses these differences side-by-side based on a review of the design science literature together with an analysis of the basic structure and knowledge involved in existing thermal simulation tools. This discussion aims to unfold a set of insights into the type of approach needed to move this research area further. It highlights the modus operandi of the building designer rather than focusing on collaborative efforts and sets up the backgrounds for designers to learn relevant concepts of building physics in an environment in which they can experiment with these concepts as ‘craftsmen’.
The present paper is an attempt to bridge the gap between building designers and simulationists by proposing a common framework for discussion. It is a positional paper written from a building designer's viewpoint that basically agrees with the proposition that design is no longer dominated by physical structure thinking but by performance and system based concerns. However, the authors still recognise the need to find appropriate criteria, which are directly related to design actions, to evaluate performance and therefore effectively relate design decisions to simulation results. The proposed framework operates within an integrated dynamic system methodology in which outputs, performance goals, optimisation and controls are dealt with at the level of the building envelope response instead of the overall building response. It is believed that the best way to set up a conversation between designers and simulationists is not to swing between the two points of view but to establish a unified framework for discussion disconnected from any specific tool or performance target.
Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education.
Much of the current building simulation research and development concentrates on improving user interfaces to simulation "engines". The goal seems to be to make the software easier to use. This begs two questions: what interface to use? And, by what criteria is software ease of use measured? What is the intelligent personal (design) assistant ? This paper 1 reports analysis of a survey of users of simulation software which aimed to determine what they seek from improvements to the product they use regularly. During January and February 1996 a telephone and + The amount of customisation of the mail survey was conducted of experienced simulation simulation package routinely undertaken. consultants in the western United States of America. This paper examines the processes used by these practitioners when they wish to maintain quality assurance in their office simulation routine. It also describes the priority placed by these practitioners on such usability features as Graphic User Interfaces, Default Values and "Prototypical" buildings.
Although ecologists have become increasingly sophisticated in applying tests for statistical significance, few are aware of the power of these tests. Statistical power is the probability of getting a statistically significant result given that there is a biologically real effect in the population being studied. If a particular test is not statistically significant, is it because there is no effect or because the study design makes it unlikely that a biologically real effect would be detected? Power analysis can distinguish between these alternatives, and is therefore a critical component of designing experiments and testing results (Toft and Shea 1983
This is the third edition of the premier professional reference on the subject of data mining, expanding and updating the previous market leading edition. This was the first (and is still the best and most popular) of its kind. Combines sound theory with truly practical applications to prepare students for real-world challenges in data mining. Like the first and second editions, Data Mining: Concepts and Techniques, 3rd Edition equips professionals with a sound understanding of data mining principles and teaches proven methods for knowledge discovery in large corporate databases. The first and second editions also established itself as the market leader for courses in data mining, data analytics, and knowledge discovery. Revisions incorporate input from instructors, changes in the field, and new and important topics such as data warehouse and data cube technology, mining stream data, mining social networks, and mining spatial, multimedia and other complex data. This book begins with a conceptual introduction followed by a comprehensive and state-of-the-art coverage of concepts and techniques. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability. relational data. -- A comprehensive, practical look at the concepts and techniques you need to get the most out of real business data. -- Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning, -- Scores of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. -- Complete classroom support for instructors as well as bonus content available at the companion website. A comprehensive and practical look at the concepts and techniques you need in the area of data mining and knowledge discovery.
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