Vol-7, Special Issue-Number4-July, 2016, pp600-608 http://www.bipublication.com
Research Article
Optimization of Building Energy Consumption through
Data Mining Using Modern Science
Navid kalani1, Mohammad Hossein Modabber1,
Hossein Kargar Jahromi2 and Mansour Darvishi Tafvizi1*
1- Medical Ethics Research Center, Jahrom University of Medical Sciences, Jahrom, Iran
2- Research center for non.Communicable Diseases, Jahrom University of Medical Sciences, Jahrom, Iran
Corresponding Author Email: tafvizi.m@gmail.comTel: (+98) 9179081621
ABSTRACT:
Today’s world unlike the past when all attentions were focused on industries such as oil and energy, has tended towards IT and Electronics; Due to the short-lived and nonrenewable energy resources; investment in science and technology to find solutions to improve the energy efficiency has found convincing justification. On the other hand, one of the most important issues to achieve the peaks of scientific progress is to keep up with the knowledge of the world and make connections between different sciences. Building industry also as one of the most influential industries in the economy is necessary to be equipped with the most advanced knowledge, to bring with it improved accuracy, quality, productivity and speed.
In Iran, statistical data indicates that large share of the energy consumption belongs to construction and housing sector and that the rate is also higher than international standards. Therefore, efforts to optimize energy consumption on a large scale can have a significant impact on total energy consumption in the country. This article with a holistic approach to building and considering three interconnected components of building design, design of the facility and residents behavior, suggests knowledge of modern data mining to discover hidden patterns and relationships between data. In order to make effective energy optimization, a two-part data mining and optimizing design is added to the building design process.
In this study, primarily by explaining the objectives and the need to discuss the issue the knowledge and data mining techniques are introduced. Then, the two most widely used algorithm in decision making tree and genetics as well as the techniques to add and absorption that have been studied in this field. Eventually, ant colony algorithms and the technique of integration model that has not been used so far in the field of building energy optimization; we have introduced it as the innovation of this research and as part of the study proposal. We hope this article is to provide the underlying condition for application of data mining knowledge related to the construction industry and taking advantage of its gains.
Keywords: buildings, data mining, the decision making tree, genetic algorithm, ant colony algorithm
INTRODUCTION:
Today, with the growing demand for energy supply, constraints of non-renewable resources and its decreasing trends, discussion such as energy efficiency, waste and energy management in buildings is of great importance and efforts have been made to improve the energy efficiency
benefits of smart buildings and energy management systems (EMS), has brought about good results; However, statistical reports indicate that more than %80 of the buildings in the country have energy-waste and this is the a matter of trivial concern. Thus, the need to adopt new methods is felt more than ever. A recent study through using innovation and taking advantage of new knowledge of data mining that is considered as recent advances in data management technology; it can be transformed to serve the construction industry by uncovering hidden patterns and relationships between data to set optimization of energy consumption efforts more efficient. The utilization of data mining techniques in the construction industry with a holistic view will consider, three intertwined components as the basis for data cache that includes building design, facility and equipment design, and behavior of residents, so this study could be important for engineers, architects, contractors, employers and residents. It is obvious that the study in this field, in addition to directing the activities of the consumption patterns’ culture-building practices can be a basis for designing for smart buildings and to help its development. It should be noted, in this article, whatever is based on studies and research expertise of the design, or is discussed and is the issue of the building design process that we hope to apply to the underlying knowledge of data mining in construction industry to benefit from its achievements and to attract experts in the other fields.
The objectives and the significance of the study:
Since one of the major challenges of this century around the world, is the issue of energy; and statistically half of all energy consumption is known to belong to the building sector [1] For years, the issue of energy efficiency in buildings is increasingly drawing interest of the experts in this area; however, despite the efforts made little improvements has been made; In other words, while housing construction sector is the largest energy consumer in Iran, the rate of consumption
is very high in comparison with other countries and international standards.
Graph 1- Energy consumption in different parts of Iran (according to the Ministry of Energy, 1385) [2] Thus, making a reform in both traditional and modern methods to preserve energy resources is necessary. This issue leads us to benefit of new data mining knowledge that is capable to discover hidden patterns from data cache recorded in the three-part design and architecture, design of the facility and residents behavior; to make the efficient ways of savings, targeted and effective. Therefore, the project of the current article with the following objectives has been studied.
1. Overall proposal to make changes in attitudes in ways of optimizing energy consumption in buildings.
2. The introduction of pattern discovery techniques of data cache and its application in the process of building design.
3. Targeted development of energy management systems in smart buildings.
4. Finding behavioral patterns of energy consumers and efficient ways of energy saving.
percent of the information to discover the knowledge and to be analyzed and evaluated. According to MIT University, the new data mining knowledge is one of ten sciences developing in the next decade makes the technological to meet the revolution. Although the concept of data mining for the first time was introduced by doctor GregoryShapiro1 at the Conference on Artificial Intelligence in 1989 and it is more than two-decade-old, however, it continues to be remembered as the foundation of new science and in some areas it has not found its place; and even the offered concept definitions of data mining are multiple. [3] The Gartner Institute has defined it as "data mining is to discover correlations, patterns and trends with significant and new that are the result of checkout process of large amounts of data stored in cache, using pattern recognition techniques with mathematical and statistical methods." [4] In another definition of data mining is considered the process to extract reliable information, from the previously unknown, understandable and reliable large databases and use it in decision-making. [5] In fact, data mining is a bridge between science of statistics, computer science, software, artificial intelligence, pattern recognition, machine learning and visual re-representations of the data. So far, the knowledge of data mining seriously, has entered branches of sciences such as marketing, finance, manufacturing, medicine, tracking, text mining, web, forecasting and organizational learning and problems. However, in some other issues related, the surveys have also been conducted sporadically. In the field of construction industry also in the international community, especially, concerning the optimization of energy consumption that is the subject of this article; Case studies have been carried out and the research ahead has provided a broader view in regard to the creation of a new study area and basis for the development of
1
- Dr. Gregory Piatetsky-Shapiro
energy consumption optimization practices of the current issue.
As was stated in the definitions, data mining emphasizes the issues such as: extracting knowledge and finding relationships and reliable patterns among data; therefore, the attitude must first be determined in regard to the building in interaction with data mining knowledge.
The building concept that forms the basis of the data cache in this research and the feedback knowledge discovery will also be discussed. It is a whole complex that its primary goal is the welfare of its inhabitants. A building to this end includes a set of internal and external building components, facilities and eventually, its inhabitants that would cover three general categories that is the building design, facility design, equipment and behavior of the residents.
Studies conducted by the Baker2 shows that these mentioned factors of energy consumption increase up to 10 times. The share of the architectural design of the building in the consumption may increase up to 2.5 times of the normal consumption and if we add to that the facilities and equipment the amount of consumption will increase equal to twice as much i.e., 5 times of normal consumption. In this case, the share of the residents also is the remaining ten times i.e., two. [6]
It should be noted that the holistic view tells us that these three categories are tied together and are not separable from each other. With this view, data mining, firstly, is to undertake knowledge discovery involved in the building design process and secondly, by pattern recognition among the behavior of residents can also have a special place in utilized buildings. It should be noted that the residents’ behavior variable which has a great impact on the study of the utilized building is out of engineering expertise discussion and status and it is related to behavioral analysis subject in the field of sociological, cultural, and psychological
2
which in turn could constitute cache of data to be a subject of independent research.
4- The building design process and data mining steps:
The stage of building design is a process in which civil engineers, architects, and contractors encounter a set of data. In this stage, first, the required data based on the building map and system simulation of the energy intake and consumption, taking into account the factors such as policy, investment, needs, demand, location, placement, form, and dimensions of the building and in cache of data are stored. This issue is included the following general steps:
1- Providing a list of statistics and general features of the target buildings that by taking into account the factors and development criteria, architecture is designed on the basis of the existing demand.
2- Surveying the facility maps and completing the
energy auditing questionnaire
3- The collection and preparation of the required energy consumption standard and climate building design and integration of theoretical and practical principles with regard to regulations 4- Obtaining the needed or demanded facility list or identifying the ratio of consumption that generally includes the units of brightness, public facilities and air conditioning and kitchen.
5- Determination of priorities and the various arrangements of the implementation stages of based on project details
6- Planning and developing a preliminary design based on the determined basic concepts,
implementation methods, and attaining the design objectives
Now, we will carry on all the data mining steps in the process of building design, step by step. In the first step, the subject should be converted to a data mining problem. This stage is extremely challenging. Since a project faces with limitations such as budget, schedule and resources; it is necessary that all stakeholders including the owners, designers, engineers and contractors have a clear understanding of the importance of a
definition of problem and failure finding from the very beginning of the project. Then, through energy simulation3 based on designed building map, a large amount of data that is required is obtained that must now be stored in the data cache.
Now the appropriate data must be selected. This step is very important because the data in the database is the raw data so cleansing the data that eliminates and data inconsistency and noise and also the integration of the multi-source data that includes a combination of the data, the data related to energy consumption optimization analysis to be retrieved from the database. The third step is the data recognition that measures such as summarizing and harmonizing the data, to provide the context for the fourth step i.e., the initial model.
Then, the primary model data that fits the data mining is converted into a form that is followed. Now comes the turn for the main process of data mining that smart procedures and data mining algorithms are used to extract patterns and the models. In this step the primary model according to the data is stabilized, data is classified and the needed model and pattern that is the same optimization pattern of energy consumption in the building; is constructed. In the eight stage, models are evaluated if there are any shortcomings; they could be eradicated.
Then, through developing models and evaluating the results, the targeted knowledge is to be discovered and it is needed to be shown visually so that the building engineer and architect can apply it in the building design.
This process and relationship between the steps are displayed in the following figures.
3
- During t he past 50 years a w ide range of building
energy simulation (BES) w it h tools such as Blast , EnergyPlus, eQUEST, TRACE, DOE2, ECOTECT have
been developed. [7] This t ools are complex
Figure 1- The relationship between the steps of data mining and its process from converting the subject of the data mining issue to presentation of knowledge
Now, in the process of building design, the two steps are added. One step is data mining and its processes and the other is the optimization of the building design, which in the following figure, these steps are displayed in the order.
Figure 2- The process of building design with a data mining approach
5- Data mining algorithms in the process of building design
Now turn to introducing the algorithms used in data mining that can help the optimal design of
the building. As it was stated after the earlier model development phase, it is the turn for the data mining main process. In this process, the algorithms are used to predict which include statistics such as regression formulas, databases technological systems such as association rules and techniques based on machine learning such as neural networks and decision making trees. Each of these algorithms with respect to the ultimate goal of data mining as well as data modeling types may be available. For example regression in predicting the future based on current data are very useful, while for data classification the decision making tree can be of great help. Also, when placing the goods on store shelves and finding out the relationship between the goods when purchasing in store chain, the dependence rule would be very useful. [3]
So far, numerous classifications have been done for data mining techniques. The following diagram shows the classification techniques in collection of approaches, the main technique and the secondary are displays.
Graph 2- The data mining techniques.
A): in the Later section in this article we will discuss them.
5-1- Decision making tree
Decision making tree is a popular technique in the field of data mining the. Its most common duty is classification. The division of data is as a regression to sub-assemblies including a homogenous condition of the target variable; the underlying purpose of the decision making tree. When the regression process was complete, decision making tree is formed.
The advantages of using the algorithms of decision making tree in data mining is to optimize its energy, in addition to finding patterns that optimize the building design process, gives us the appropriate class of comfort; It is also made faster and it is easier to interpret the model. Predictions based on decision making tree are more effective and efficient. There are various methods to grow a tree and various formulas can be used to determine how to divide the tree. Each path in the tree from the root node to leaves represents an extracted rule.
The data of decision making tree algorithm can be in form of discrete and continuous parameters. In data mining discrete features, the algorithm makes predictions based on the relationships between the entry columns in a series and complements a column that will do as predictable column is selected. In continuous data mining algorithm parameters, linear regression is used to determine the place of the tree distribution. With the formation of trees as for per column we can make the input data including nested tables as well as a predictable set.
The data classification by decision making trees is a two-stage process. In the first stage, a model is made based on a classification algorithm in accordance with the related data of the training set (random set of database) this stage is also called learning stage. In the second stage, learning is done through a function , that can
predict a class label of each record x from the database. Learning stage by itself is done in two steps, the growth and pruning. During the training
process, decision making tree algorithm should frequently be the most effective method to find for splitting a record collection to the children. Pruning stage is done to prevent excessive processing or over fitting. Various criteria is chosen to determine an attribute that according to which split must be done that are selected according to field type of classification.[11] Case study in 2007 on the construction of offices and laboratories with a population of more than 100 residents was done by Young Goa at the University of Ireland. 20 million data from sensors of monitoring temperature, levels of carbon dioxide and moisture, decision making tree algorithm was an important achievement, including the relationship between the location of the building and external factors on heating and cooling systems’ design, as well as the needed sensors for the network in similar buildings. [12] 5-2- Genetic algorithm
A genetic algorithm is an approach to model the natural evolution of creatures. The application of this algorithm, mostly observes the responses to confused spaces that have local relative minimum and maximum points. Among the advantages of genetic algorithm is to reduce the likelihood of getting caught in the trap of a local optimum. This algorithm, begins the search with a random sample population and then, using the random operations and based on the values of objective function follows the optimization. This algorithm is an attempt to model the production process based on Darwinian Theory of natural selection and with three widely used concepts of individual4, population and generation uses random functions with titles crossover, mutation and Fitness Proportionate reproduction.
In discussion terms of further optimization of binary genetic algorithm is used, in which every person is called a binary string called chromosome is introduced. The chromosome in a
4
particular order (the programmer determines) encodes the involved parameters in the problem. Each chromosome is made up of a number of genes and to determine the suitability of each individual input, values equal to the values encoded by chromosomes (the objective function at that point) is assumed.
In the genetic algorithm, the procedure of evolution of the generations are well controlled so that the chromosomes to be recognized as to take part in the production of the next-generation. Crossover operator is also operate on the two random chromosomes and by transforming their genes from the random spot makes two new chromosomes.
Case study have been conducted by Mohammad Hassan Saeedi and his colleagues based on genetic algorithm with the aim of determining the size of the windows of a building in order to provide optimum conditions for lighting and heating (heating and cooling). In this study, about 17 million data in the discrete space entered the data mining process and the results, optimum dimensions for the different sides of the building windows under study were revealed. This study showed that unexpected responses may appear in the data mining process that even cannot be seen by the eyes of a HVAC engineer taking into account the effects of energy consumption. [13] 5.3 Addition and absorption method (A & A) The data, recorded by the sensors, mainly is stored for tables with columns and information is stored in different formats. Addition method and the absorption are methods that by increasing flexibility in the creation of Relational database management system (RDBMS);the essential tables form «element», «attribute» and «value» to solve the problems arising from multiple tables stored in the database and existence of differences between output formats from various sensors. In addition, given the circumstances, conditions and multiple sensors installed, this method is able makes it easier to create relational data model. [14]
Graph 3- An example of a simple data model of a building using A & A in which the text data transmission to the columns of element, attribute and value can develop an integrated building model and recorded data from the sensors. [15]
6- The Proposed algorithms:
As it was stated various algorithms and techniques have been introduced and have been used in data mining science, so far. But the idea that which one is effective in improving the optimization of energy in building should be studied and become familiar with both fields. In this article, in addition to the introduced algorithms there is also a hybrid technique and an algorithm, which is the result on the basis of studies conducted; we propose briefly, as the description in details of the case studies is out of the scope of this brief.
6-1-Ant colony algorithm
The ant colony inspired by the instincts of community problem solving nature of ants; one of the most studied systems in finding the shortest path is an optimization problem. In the ant algorithm to select the next node they use the pheromone amount and distance between nodes, from which has a direct relationship with the amount of pheromone on the ridge and the reverse distance between nodes. Since building energy optimization in different dimensions is under investigation, the algorithm is used in the optimal design of the facility and exploration to find points of wasting energy; and studies indicate the speed and power of this algorithm.
6-2- A hybrid model
possibly different, sometimes in spite of holistic look and taking measures to prevent again it is possible; this phenomenon occurs, one of the effective suggestions to determine some overall optimization is that an optimization algorithm generalized (GEO) that is a super exploration algorithm to solve problems of optimization and has a very high speed of convergence along with the Cellular Learning Automata (CLA) to improve its performance that is not efficient alone in conditions by getting help of Cellular Learning Automata. In this regard, a hybrid model eliminates the problem of getting caught in local optimum points and it evaluates a range of problems to find the answer in a better shape.
7- CONCLUSION
The aim of this paper is to present a new approach with a focus on improving energy efficiency in building design using optimization methods and data mining algorithms. From the above discussion, it can be inferred that a series of data mining techniques are worthwhile efforts to discover solutions to optimize energy consumption in buildings. It should be noted that science of data mining in other parts of the building engineering process including building reinforcement can also be used.
In this article, with a holistic and integrated view of the building and the three components of building design, facility design and behavior of residents, while introducing the science of data mining in the expression of efficient algorithms to optimize energy efficiency in buildings, confirmed case studies, eventually, the proposed techniques were expressed. The results obtained of this study show that in order to succeed the design and construction process as a strategy for energy saving and coordination between architecture and building engineering systems and treatment facilities and residents behavior, it is needed to discover efficient models from among data cache that this is the important duty of data mining science.
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15. Y
