A Novel Method for Detecting Abnormal Energy Data in Building Energy Monitoring System

This paper presents a novel abnormal data detecting algorithm based on the first order difference method, which could be used to find out outlier in building energy consumption platform real time. The principle and criterion of methodology are discussed in detail. The results show that outlier in cumulative power consumption could be detected by our method. 1. Introduction Building energy consumption represents a significant percentage of national energy consumption in many countries; the reaching figures of energy used in buildings in comparison with the total national consumption are considered to be 25% for Japan, 28% for China, 37% for European Union, and 40% for the United States [1–4]. Furthermore, the rise of energy demand in buildings will continue in the near future because of the growing use of buildings and increasing demand for improved building comfort levels [5]. In this scenario, energy efficiency in facilities is a prime objective of energy policy, and the energy efficiency of buildings is of prime concern in both developing and developed countries for anyone who wishes to identify energy savings. Energy monitoring can help to achieve energy savings and improve the quality of the energy supply; therefore, it can be of strategic importance. The purpose of monitoring building energy consumption is to get data, which is to provide greater insight into how a building consumes energy and achieve a better understanding of the energy usage. Once the dynamic of the energy consumption of a building is known, it is possible to analyze what improvements are likely to be most effective in reducing consumption. There are already some intelligent building energy consumption monitoring platforms designed in order to collect the energy data in buildings, especially for large public buildings [6–8], which include hotels, hospitals, convenience stores, and government office buildings. However, there always exits abnormal data in the process of data acquisition, outlier, which reflects the very small or very large data compared to other normal data, as shown in Figure 1. The outlier must be removed because it will lead to the increasing of error during the data processing in the server, even cannot be calculated. Figure 1: Outlier in building energy consumption monitoring system. 2. Related Work In [9], the researchers developed neural network algorithm to check if there is any energy consumption data by using predicting the energy consumption from collected previous data. If the predicted data is lower or higher than the setting thresholds, which is