Abstract
The data quality of building energy consumption monitoring platform is generally not high and there are a lot of problem data. This paper proposes an identification method of implicit error energy consumption data based on the overall usage characteristic. In this method, we connect hourly energy consumption data into lines. According to the influencing factors of the building operation, we classify the energy-usage mode. By using the clustering method, we identify partial abnormal data. Then we count the slopes of historical energy consumption data characteristic lines and compare the time-varying characteristic lines of real-time with the historical characteristic lines under the same energy-usage mode. By using the energy consumption data of an office building, we verify the reliability of this method in identifying the abnormal energy consumption data of lighting and socket. This method improves the quality of data and will make the energy monitoring platform more efficient in building energy conservation.
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Acknowledgements
The study is supported by the National Key R&D Program of China (Grant No. 2017YFC0704200).
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Ma, L., Xu, Y., Qin, Y., Zhang, J. (2019). Identifying Abnormal Energy Consumption Data of Lighting and Socket Based on Energy Consumption Characteristics. In: Fang, Q., Zhu, Q., Qiao, F. (eds) Advancements in Smart City and Intelligent Building. ICSCIB 2018. Advances in Intelligent Systems and Computing, vol 890 . Springer, Singapore. https://doi.org/10.1007/978-981-13-6733-5_6
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DOI: https://doi.org/10.1007/978-981-13-6733-5_6
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