Abstract
Since the beginning of data mining technologies, buildings have become not just energy-intensive but also information-centric. Data mining technologies have been widely used to utilize the huge quantities of buildings’ operational data to improve their energy systems. Conventional benchmarking of buildings’ energy performance reflects a variety of parameters, such as the number of inhabitants, the environment, the energy efficiency of equipment utilized, and the adjustment of internal temperature. These various elements are then assigned weights to generate a single general indicator. This study presents a reasonable benchmark assessment methodology of conventional buildings’ energy usage based on a data-mining algorithm for acquiring more specific information, like the energy management efficacy of a building, and aiming at the problem of ineffective use of large amounts of energy consumption in public buildings. A mathematical-statistical approach and a data-mining tool are used to analyse the data. The degree of connection between numerous influencing variables (i.e., characteristic parameters) and building’s energy usage is determined using grey correlation analysis. In this work, we have used an enhanced Apriori algorithm to identify the link between the different forms of systems in the same area. In short, the fundamental idea and process of the Apriori algorithm are presented, and preliminary designs of the preprocessing of experimental data as well as the analysis methods are studied to analyse the outcome of the proposed work.
Highlights
Every technological improvement produces a series of products and services as part of the social evolution, but it leads to a rapid increase in resource and energy consumption
Many simulation tests on traditional building energy usage were conducted using an improved Apriori algorithm and a combined decision tree procedure
The traditional buildings are classified, and the building energy consumption reference value is determined and evaluated using data mining techniques. e classification of buildings may be fine-tuned with this technique, and the efficiency of building energy consumption reference values can be enhanced
Summary
Every technological improvement produces a series of products and services as part of the social evolution, but it leads to a rapid increase in resource and energy consumption. Data mining methods have been widely utilized to unlock the values of enormous volumes of building operation data, as the author in [17] worked to improve the operational performance of building energy systems. E application of data mining in the field of building HVAC is mainly divided into data mining framework process, preprocessing and specific application, etc It mostly entails the analysis of building energy consumption data, problem diagnosis and detection, and system and data operation and control optimization, among other things. (3) irdly, data mining methods have been widely utilized to identify actual values of huge quantities of building information, in order to improve the overall performance of building energy systems. (4) different evaluation methods are used to confirm that the data are evaluated using a mathematical statistics technique and a data-mining algorithm in order to improve the building energy systems.
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